Insects have small brains and heuristics or ‘rules of thumb’ are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary, heuristic decisions are also deemed to impose a compromise in decision accuracy. Using examples from object optimization behaviour in insects, we will argue that heuristics do not inevitably imply (...) a lower computational burden or lower decision accuracy. We also show that heuristic optimization may be driven by certain features of the optimization problem itself: the properties of the object being optimized, the biology of the insect, and the properties of the function being optimized. We also delineate the structural conditions under which heuristic optimization may achieve accuracy equivalent to or better than more fine-grained and onerous optimization methods. (shrink)

Resource saving has become an integral aspect of manufacturing in industry 4.0. This paper proposes a multisystem optimization algorithm, inspired by implicit parallelism of heuristic methods, to solve an integrated production scheduling with resource saving problem in textile printing and dyeing. First, a real-world integrated production scheduling with resource saving is formulated as a multisystem optimization problem. Then, the MSO algorithm is proposed to solve multisystem optimization problems that consist of several coupled subsystems, and each of (...) the subsystems may contain multiple objectives and multiple constraints. The proposed MSO algorithm is composed of within-subsystem evolution and cross-subsystem migration operators, and the former is to optimize each subsystem by excellent evolution operators and the later is to complete information sharing between multiple subsystems, to accelerate the global optimization of the whole system. Performance is tested on a set of multisystem benchmark functions and compared with improved NSGA-II and multiobjective multifactorial evolutionary algorithm. Simulation results show that the MSO algorithm is better than compared algorithms for the benchmark functions studied in this paper. Finally, the MSO algorithm is successfully applied to the proposed integrated production scheduling with resource saving problem, and the results show that MSO is a promising algorithm for the studied problem. (shrink)

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), (...) we explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)

Clearing algorithms are at the core of modern payment systems, facilitating the settling of multilateral credit messages with (near) minimum transfers of currency. Traditional clearing procedures use batch processing based on MILP - mixed-integer linear programming algorithms. The MILP approach demands intensive computational resources; moreover, it is also vulnerable to operational risks generated by possible defaults during the inter-batch period. This paper presents TORC3 - the Token-Ring Clearing Algorithm for Currency Circulation. In contrast to the MILP approach, TORC3 is a (...) real time heuristic procedure, demanding modest computational resources, and able to completely shield the clearing operation against the participating agents’ risk of default. (shrink)

For enhancing the stability of the microgrid operation, this paper proposes an optimization model considering the small-signal stability constraint. Due to the nonsmooth property of the spectral abscissa function, the droop controller parameters’ optimization is a nonsmooth optimization problem. The Sequential Quadratic Programming with Gradient Sampling is implemented to optimize the droop controller parameters for solving the nonsmooth problem. The SQP-GS method can guarantee the solution of the optimization problem globally and efficiently converges to stationary points (...) with probability of one. In the current iteration, the gradient of the nonsmooth function can be evaluated on a set of randomly generated nearby points by computing closed-form sensitivities. A test on the microgrid system shows that the optimality and the efficiency of the SQP-GS are better than those of the heuristic algorithms. (shrink)

. The defects in macro traffic model research are pointed out firstly. In order to remedy these defects, traffic movements at the grid intersection were analyzed, and with the basic framework of the traffic transmission model, the new macro traffic model used in the paper for control simulation and evaluation has been proposed. Secondly, the bi-level optimization control model is proposed, using minimal delay and maximal throughput as its upper objectives, and optimal traffic coordination on both sides as the (...) lower objective. A corresponding heuristic ant algorithm is subsequently designed to solve the control model. Finally, the proposed method is tested at a suppositional road network, under three different demand scenarios, compared with Transyt-7F. The results show that the proposed method has better performance, especially under high demand scenarios. (shrink)

This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra delays can be introduced between successive operations of the same job. It belongs to a category of problems known as NP-hard problem due to large solution space. Biogeography-based optimization is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon in 2008 (...) to optimize hard combinatorial optimization problems. We propose a hybrid biogeography-based optimization algorithm for solving the job shop scheduling problem with additional time lag constraints with minimization of total completion time. In the proposed HBBO, the effective greedy constructive heuristic is adapted to generate the initial population of habitat. Moreover, a local search metaheuristic is investigated in the mutation step in order to ameliorate the solution quality and enhance the diversity of the population. To assess the performance of HBBO, a series of experiments on well-known benchmark instances for job shop scheduling problem with time lag constraints is performed. (shrink)

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance (...) and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy. (shrink)

A comment on Paul Schoemaker's target article in Behavioral and Brain Sciences, 14 (1991), p. 205-215, "The Quest for Optimality: A Positive Heuristic of Science?" (https://doi.org/10.1017/S0140525X00066140). This comment argues that the optimizing model of decision leads to an infinite regress, once internal costs of decision (i.e., information and computation costs) are duly taken into account.

This paper examines the strengths and weaknesses of one of science's most pervasive and flexible metaprinciples;optimalityis used to explain utility maximization in economics, least effort principles in physics, entropy in chemistry, and survival of the fittest in biology. Fermat's principle of least time involves both teleological and causal considerations, two distinct modes of explanation resting on poorly understood psychological primitives. The rationality heuristic in economics provides an example from social science of the potential biases arising from the extreme flexibility (...) of optimality considerations, including selective search for confirming vidence, ex post rationalization, and the confusion of prediction with explanation. Commentators are asked to reflect on the extent to which optimality is an organizing principle of nature, a set of relatively unconnected techniques of science, a normative principle for rational choice and social organization, a metaphysical way of looking at the world, or something else still. (shrink)

Uninhabited combat air vehicle path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization algorithm based on the particle swarm optimization algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local (...) search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms. (shrink)

The field service scheduling problem is the key problem in field services. Field service pays particular attention to customer experience, that is, customer satisfaction. Customer satisfaction described by customer behavior characteristics based on the prospect theory is considered as the primary optimization goal in this paper. The knowledge of the insertion feasibility on the solution is analysed based on the skill constraint and time window. According to the knowledge, an initialization method based on the nearest heuristic algorithm is (...) constructed. Based on the prior knowledge of the FSSP and the endowment of the Fruit Fly Optimization Algorithm, two operators are defined according to the matrix encoding method. Based on these two operators, three search strategies are then proposed, and the smell-based search strategy and vision-based search strategy for the FOA are redesigned. To verify the performance of the algorithms, the proposed operators and strategies are tested and analysed in the well-known benchmark. Through comparison with the state-of-the-art algorithms, the results show that the proposed HFOA is an effective and efficient method to solve the FSSP with customer satisfaction. (shrink)

With the increasing proportion of the logistics industry in the economy, the study of the vehicle routing problem has practical significance for economic development. Based on the vehicle routing problem, the customer presence probability data are introduced as an uncertain random parameter, and the VRP model of uncertain customers is established. By optimizing the robust uncertainty model, combined with a data-driven kernel density estimation method, the distribution feature set of historical data samples can then be fitted, and finally, a distributed (...) robust vehicle routing model for uncertain customers is established. The Q-learning algorithm in reinforcement learning is introduced into the high-level selection strategy using the hyper-heuristic algorithm, and a hyper-heuristic algorithm based on the Q-learning algorithm is designed to solve the problem. Compared with the certain method, the distributed robust model can effectively reduce the total cost and the robust conservatism while ensuring customer satisfaction. The improved algorithm also has good performance. (shrink)

This article uses a research method that combines theoretical research and empirical analysis. It first introduces the relevant theories of energy service city distribution sites in the context of e-commerce and then the types of energy service city distribution sites and the composition of energy service city distribution systems. The network layout of the service city distribution site and the location objectives, principles, and processes of the model is studied to determine the network layout plan of the energy service city (...) distribution system in this paper. This paper fully considers the characteristics of the network operation mode of the energy service city distribution site and establishes an optimization model for the location selection and vehicle routing of the distribution center with the lowest total system cost under the simultaneous delivery service mode; based on the hierarchical solution strategy, a combination of deep learning is designed. Algorithms mainly include two-stage hybrid heuristic algorithm of cluster analysis, maximum coverage and genetic algorithm; simulation analysis is conducted to verify the effectiveness of the model and algorithm by data simulation, finally get the integrated optimization plan of distribution center location and routing, and put forward the operation strategy through the result expansion analysis. This paper studies the planning model based on the network layout planning of the energy service city distribution system under the e-commerce environment, aiming to promote the breakthrough development of urban smart logistics and prove the importance of the energy service city distribution station network layout planning. The purpose and results of the research are to reduce traffic and environmental pressures, achieve joint direct distribution, improve the efficiency of urban logistics and distribution, and solve the problem of the last mile of the city. (shrink)

In this article the theoretic evolution and the empirical-experimental efforts that have led to the affirmation of the bounded/procedural rationality paradigm are discussed. Moreover, the debate on supporters of the "optimization" approach and supporters of the "bounded/procedural rationality" approach is traced, highlighting the irreconcilability of these two approaches and, in retort, a solid defense against a merely "reductionist" attempt of the innovative context of the Simonian theory. Critically going over the debate on decision dynamics, it becomes clear how, due (...) to the uncertain nature of rational processes, it is impossible to establish the decision-making best way. The imperfect character of individual choice is explained by how the decider identifies a solution that appears satisfying in that moment due to cognitive and temporal limits. (shrink)

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked (...) by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. (shrink)

The paper shows why and how an empirical study of fast-and-frugal heuristics can provide norms of good reasoning, and thus how (and how far) rationality can be naturalized. We explain the heuristics that humans often rely on in solving problems, for example, choosing investment strategies or apartments, placing bets in sports, or making library searches. We then show that heuristics can lead to judgments that are as accurate as or even more accurate than strategies that use more information and computation, (...) including optimization methods. A standard way to defend the use of heuristics is by reference to accuracy-effort trade-offs. We take a different route, emphasizing ecological rationality (the relationship between cognitive heuristics and environment), and argue that in uncertain environments, more information and computation are not always better (the “less-can-be-more” doctrine). The resulting naturalism about rationality is thus normative because it not only describes what heuristics people use, but also in which specific environments one should rely on a heuristic in order to make better inferences. While we desist from claiming that the scope of ecological rationality is unlimited, we think it is of wide practical use. (shrink)

The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far in the field. It (...) is also important to analyse the applicability of the premise of the dominance techniques applied so far in order to implement the technique. The work presents a systematic review of diploid genetic algorithms, examines the premise of the dominance relation used in different works and discusses the future scope. The work also discusses the biological basis of evaluating dominance. The work is important as the future of machine learning relies on techniques that are robust as well as efficient. (shrink)

Traditional views of rationality posit general-purpose decision mechanisms based on logic or optimization. The study of ecological rationality focuses on uncovering the “adaptive toolbox” of domain-specific simple heuristics that real, computationally bounded minds employ, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.

We formulate the problem of permuting a matrix to block angular form as the combinatorial minimization of an objective function. We motivate the use of simulated annealing (SA) as an optimization tool. We then introduce a heuristic temperature dependent penalty function in the simulated annealing cost function, to be used instead of the real objective function being minimized. Finally we show that this temperature dependent penalty function version of simulated annealing consistently outperforms the standard simulated annealing approach, producing, (...) with smaller running times, better solutions. We believe that the use of a temperature dependent penalty function may be useful in developing SA algorithms for other combinatorial problems. -/- . (shrink)

Beyond the predominant paradigm of an essentially rational human cognition, based on the classical binary logic, we want to propose some reflections that are organized around the intuition that the representations we have of the world are weighted with appreciations, for example affective ones. resulting from our integration into a social environment. We see these connotations as essentially ternary in nature, depending on the concepts underlying neutrosophy: either positive, negative or neutral. This form of representation would then influence the very (...) nature of the cognitive process, which in complex real-world situations, has to deal with problems of a combinatorial nature leading to a number of cases too large for our abilities. Forced to proceed by shortcuts on the basis of heuristics, cognition would use these assessments of the representations it manipulates to decide whether partial solutions are attractive for solving the problem or on the contrary are judged negative and are then quickly rejected. There is still the case of a neutral weighting that allows processing to continue. Thus a neutrosophical conception of our representations of the world explains how our cognition functions in its treatment of combinatorial problems in the form of producing processing accelerating heuristics, both in terms of partial solutions selection and processing optimization. (shrink)

In a previous paper, we proposed a solution to navigation of a mobile robot. In our approach, we formulated the following two problems at each time step as discrete optimization problems: 1) estimation of position and direction of a robot, and 2)action decision. While the results of our simulation showed the effectiveness of our approach, the values of weights in the objective functions were given by a heuristic method. This paper presents a theoretical method using reinforcement learning for (...) adjusting the weight parameters in the objective function that includes pieces of heuristic knowledge on the action decision. In our reinforcement learning, the expectation of a reward given to a robot’s trajectory is defined as the value function to maximize. The robot’s trajectories are generated stochastically because we used a probabilistic policy for determining actions of a robot to search for the global optimal trajectory. However, this decision process is not a Markov decision process because the objective function includes an action at the previous time. Thus, Q-learning, which is a conventional method of reinforcement learning, cannot be applied to this problem. In this paper, we applied Williams’s episodic REINFORCE approach to the action decision and derived a learning rule for the weight parameters of the objective function. Moreover, we applied the stochastic hill-climbing method to maximizing the value function to reduce computation time. The learning rule was verified by our experiment. (shrink)

The socioeconomic and ethnic characteristics of parents are some of the most important correlates of adverse health outcomes in childhood. However, the relationships between ethnic, economic, and behavioral factors and the health outcomes responsible for this pervasive finding have not been specified in child health epidemiology. The general objective of this paper is to propose a theoretical approach to the study of maternal behaviors and child health in diverse ethnic and socioeconomic environments. The specific aims are: (a) to describe a (...) causal pathway between the utility that women obtain through work outside the home and through child care and disease hazard rates in childhood using an optimization model; (b) to specify the influence of ethnic and socioeconomic factors on model constraints; (c) to use the model as a tool to learn about how different combinations of maternal wage labor and child care time might influence child health outcomes in diverse social contexts; (d) to identify parameters that will require measurement in future research; (e) to discuss research strategies that will enable us to obtain these measurements; and (f) to discuss the implications of the model for biostatistical modeling and public health intervention. Optimization models are powerful heuristic tools for understanding how ethnic, environmental, family, and personal characteristics can place important constraints on both the quality and quantity of care that women can provide to their children. They provide a quantitative appreciation for the difficult trade-offs that most women face between working in order to purchase basic goods that children cannot do without (e.g., food, clothing, shelter, health insurance), and increasing offspring well-being through child care (e.g., training in social skills, affection, protection from environmental hazards, help with homework). (shrink)

It is often claimed that formal and optimizing norms of the standard conception of rationality and the heuristics of the bounded rationality approach are at odds with one another. This claim, I arg...

In this paper, we propose the c AS, a new ACO algorithm, and evaluate the performance using TSP instances available at TSPLIB. The results show that c AS works well on the test instances and has performance that may be one of the most promising ACO algorithms. We also evaluate c AS when it is combined with LK local search heuristic using larger sized TSP instances. The results also show promising performance. c AS introduced two important schemes. One is (...) to use the colony model divided into units, which has a stronger exploitation feature while maintaining a certain degree of diversity among units. The other is to use a scheme, we call cunning, when constructing new solutions, which can prevent premature stagnation by reducing strong positive feedback to the trail density. (shrink)

The identification of regularities in time-dependent functional structures leads to turn patterns, observed according to a given time resolution, into functional attractors on which it is first possible to found any complex system. Rationality is introduced under the form of probabilities for functions to make up a given attractor beyond the first rough descriptive pattern. These physically characterized attractors are the medium enabling the definition of value as an extension of the Prospect Theory overall utility, considering that the actions produced (...) by their functions are outcomes which are modulated by PT psychological value functions and an operational value appears naturally as an optimization taking account of diminishing sensitivity to action efficiency. Nevertheless, this diminishing sensitivity, when applied to negative actions, introduces also a bias of negative externalities underestimation. An attractor-based complex system evolution in an industrial context is described according to Manufacturing Readiness Level and to the main milestones of an industrial project, corresponding to singular points of maturity expressed in terms of probability—or decision—weighting, up to the system release to the market. So, a maturity assessment heuristics is proposed both for the system development and for its marketing. The supply and demand law is established, highlighting that the ideal case of a purely mature economy can be a good approximation reducing the complexity of the exchanges analysis. Equilibria are discussed as depending on an initial point from which the usual Gaussian probability distribution and a speculative weight qualify equilibrium. (shrink)

In this paper I examine the role of optimality reasoning in Aristotle’s natural science. By “optimality reasoning” I mean reasoning that appeals to some conception of “what is best” in order to explain why things are the way they are. We are first introduced to this pattern of reasoning in the famous passage at Phaedo 97b8-98a2, where (Plato’s) Socrates invokes “what is best” as a cause (aitia) of things in nature. This passage can be seen as the intellectual ancestor of (...) Aristotle’s own principle, expressed by the famous dictum “nature does nothing in vain but always what is best for the substance from among the possibilities concerning each kind of animal” (Progression of Animals II, 704b12-18). The paper is focused around exploring three questions that arise in connection with Aristotle’s use of this optimality principle: (1) How do we understand the concept of “the best” at work in the principle? (2) How does Aristotle conceive of “the range of possibilities”? And, finally, (3) what role does optimality reasoning play in Aristotle’s natural science? Is it a special form of demonstration in which the optimality principle functions as one of its premises, or is it a heuristic device that helps uncover those causally relevant features of a natural substances that ultimately serve as middle terms in demonstrations? In the final section I return to the comparison between Plato and Aristotle and argue that, while both see the natural world as the product of an optimizing agent and while both see this assumption as licensing a pattern of reasoning that appeals to a certain conception of “the best”, they disagree fundamentally over what the optimization agent is and how it operates. Thus, despite their general agreement, it would be a mistake to think that Aristotle simply took over Plato’s use of optimality reasoning without significant modifications. (shrink)

During each school semester, students face an onslaught of material to be learned. Students work hard to achieve initial mastery of the material, but when they move on, the newly learned facts, concepts, and skills degrade in memory. Although both students and educators appreciate that review can help stabilize learning, time constraints result in a trade-off between acquiring new knowledge and preserving old knowledge. To use time efficiently, when should review take place? Experimental studies have shown benefits to long-term retention (...) with spaced study, but little practical advice is available to students and educators about the optimal spacing of study. The dearth of advice is due to the challenge of conducting experimental studies of learning in educational settings, especially where material is introduced in blocks over the time frame of a semester. In this study, we turn to two established models of memory—ACT-R and MCM—to conduct simulation studies exploring the impact of study schedule on long-term retention. Based on the premise of a fixed time each week to review, converging evidence from the two models suggests that an optimal review schedule obtains significant benefits over haphazard (suboptimal) review schedules. Furthermore, we identify two scheduling heuristics that obtain near optimal review performance: (a) review the material from μ-weeks back, and (b) review material whose predicted memory strength is closest to a particular threshold. The former has implications for classroom instruction and the latter for the design of digital tutors. (shrink)

This paper studies a special scheduling problem under hierarchical management in nurse staff. This is a more complex rostering problem than traditional nurse scheduling. The first is that the rostering requirements of charge nurses and general nurses are different under hierarchical management. The second is that nurses are preferable for relative fair rather than absolute fair under hierarchical management. The model aims at allocating the required workload to meet the operational requirements, weekend rostering preferences, and relative fairness preferences. Two hybrid (...) heuristic algorithms based on multiobjective grey wolf optimizer and three corresponding single heuristic algorithms are employed to solve this problem. The experimental results based on real cases from the Third People’s Hospital, Panzhihua, China, show that MOGWO does not as good as it does on other engineering optimization. However, the hybrid algorithms based on MOGWO are better than corresponding single algorithms on generational distance and spacing of Pareto solutions. Furthermore, for relative fair rostering objective, NSGAII-MOGWO has more power to find the optimal solution in the dimension of relative fairness. (shrink)

The article develops and justifies, on the basis of the epistemological argumentation theory, two central pieces of the theory of evaluative argumentation interpretation: 1. criteria for recognizing argument types and 2. rules for adding reasons to create ideal arguments. Ad 1: The criteria for identifying argument types are a selection of essential elements from the definitions of the respective argument types. Ad 2: After presenting the general principles for adding reasons (benevolence, authenticity, immanence, optimization), heuristics are proposed for finding (...) missing reasons, for deductive arguments, e.g., semantic tableaux are suggested. (shrink)

En este trabajo se presenta una comparación de los tiempos de respuesta, optimización de la ruta y complejidad del grafo en métodos de planificación de trayectoria para robots móviles autónomos. Se contrastan los desarrollos de Voronoi, Campos potenciales, Roadmap probabilístico y Descomposición en celdas para la navegación en un mismo entorno y validándolos para un número variable de obstáculos. Las evaluaciones demuestran que el método de generación de trayectoria por Campos Potenciales, mejora la navegación respecto de la menor ruta obtenida, (...) el método Rapidly Random Tree genera los grafos de menor complejidad y el método Descomposición en celdas, se desempeña con menor tiempo de respuesta y menor coste computacional. Palabras Clave: optimización, trayectoria, métodos de planificación, robots móviles. Referencias [1]H. Ajeil, K. Ibraheem, A. Sahib y J. Humaidi, “Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm, ” Applied Soft Computing, vol. 89, April 2020. [2]K.Patle, G. Babu, A. Pandey, D.R.K. Parhi y A. Jagadeesh, “A review: On path planning strategies for navigation of mobile robot,” Defence Technology, vol. 15, pp. 582-606, August 2019. [3]T. Mack, C. Copot, D. Trung y R. De Keyser, “Heuristic approaches in robot path planning: A survey,” Robotics and Autonomous Systems, vol. 86, pp. 13-28, December 2016. [4]L. Zhang, Z. Lin, J. Wang y B. He, “Rapidly-exploring Random Trees multi-robot map exploration under optimization framework,” Robotics and Autonomous Systems, vol. 131, 2020. [5]S. Khan y M. K. Ahmmed, "Where am I? Autonomous navigation system of a mobile robot in an unknown environment," 2016 5th International Conference on Informatics, Electronics and Vision, pp. 56-61, December 2016. [6]V. Castro, J. P. Neira, C. L. Rueda, J. C. Villamizar y L. Angel, "Autonomous Navigation Strategies for Mobile Robots using a Probabilistic Neural Network," IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 2795-2800, Taipei, 2007. [7]Y. Li, W. Wei, Y. Gao, D. Wang y C. Fan, “PQ-RRT*: An improved path planning algorithm for mobile robots,” Expert Systems with Applications, vol. 152, August 2020. [8]A. Muñoz, “Generación global de trayectorias para robots móviles, basada en curvas betaspline,” Dep. Ingeniería de Sistemas y Automática Escuela Técnica Superior de Ingeniería Universidad de Sevilla, 2014. [9]H. Montiel, E. Jacinto y H. Martínez, “Generación de Ruta Óptima para Robots Móviles a Partir de Segmentación de Imágenes,” Información Tecnológica, vol. 26, 2015. [10] C. Expósito, “Los diagramas de Vornooi, la forma matemática de dividir el mundo,” Dialnet, Diciembre 2016. (shrink)

Why would decision makers adopt heuristics, priors, or in short “habits” that prevent them from optimally using pertinent information—even when such information is freely-available? One answer, Herbert Simon’s “procedural rationality” regards the question invalid: DMs do not, and in fact cannot, process information in an optimal fashion. For Simon, habits are the primitives, where humans are ready to replace them only when they no longer sustain a pregiven “satisficing” goal. An alternative answer, Daniel Kahneman’s “mental economy” regards the question valid: (...) DMs make decisions based on optimization. Kahneman understands optimization not differently from the standard economist’s “bounded rationality.” This might surprise some researchers given that the early Kahneman, along with Tversky, have uncovered biases that appear to suggest that choices depart greatly from rational choices. However, once we consider cognitive cost as part of the constraints, such biases turn out to be occasional failures of habits that are otherwise optimal on average. They are optimal as they save us the cognitive cost of case-by-case deliberation. While Kahneman’s bounded rationality situates him in the neoclassical economics camp, Simon’s procedural rationality echoes Bourdieu’s “habitus” camp. To abridge the fault line of the two camps, this paper proposes a “two problem areas hypothesis.” Along the neoclassical camp, habits satisfy wellbeing, what this paper calls “substantive satisfaction.” Along the Bourdieu camp, habits satisfy belonging, love, and bonding with one’s environment, what this paper calls “transcendental satisfaction.”. (shrink)

In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm based on (...) elite heuristic operation and migration strategy and the improved coyote optimization algorithm based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search, and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology. (shrink)

Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time-consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that (...) the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small-size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time. (shrink)

The recent rapid development of information technology, such as sensing technology, communications technology, and database, allows us to use simulation experiments for analyzing serious accidents caused by hazardous chemicals. Due to the toxicity and diffusion of hazardous chemicals, these accidents often lead to not only severe consequences and economic losses, but also traffic jams at the same time. Emergency evacuation after hazardous chemical accidents is an effective means to reduce the loss of life and property and to smoothly resume the (...) transport network as soon as possible. This paper considers the dynamic changes of the hazardous chemicals’ concentration after their leakage and simulates the diffusion process. Based on the characteristics of emergency evacuation of hazardous chemical accidents, we build a mixed-integer programming model and design a heuristic algorithm using network optimization and diffusion simulation. We then verify the validity and feasibility of the algorithm using Jinan, China, as a computational example. In the end, we compare the results from different scenarios to explore the key factors affecting the effectiveness of the evacuation process. (shrink)

In multiple robot systems, the problem of allocation of complex tasks to heterogeneous teams of robots, also known as the multiple robot coalition formation problem, has begun to receive considerable attention. Efforts to address the problem range from heuristics based approaches that search the subspaces of the coalition structure to evolutionary learning approaches. Conventional approaches typically strive to optimize a single objective function such as the number of tasks executed or the time required to execute all tasks, or a weighted (...) function of such objectives. In real world applications, objectives such as minimizing distance traveled and maximizing the number of tasks completed are often conflicting in nature. The coalition formation problem thus naturally lends itself to a multi-objective optimization approach based on evolutionary learning. In this paper, we formulate the problem of mapping coalitions of robots to a set of tasks as a multi-objective optimization problem and propose a variant of non-dominated sorting genetic algorithm to arrive at trade-off solutions. Additionally, we extend the solution to domains where robot resources are non-additive. Simulations demonstrate the efficacy of the proposed approach in generating the set of Pareto-optimal solutions. (shrink)

This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large (...) number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks. (shrink)

The debate concerning human rationality has been revolving around four main standpoints: 1) Unbounded rationality, 2) Optimization under constraints, 3) Heuristics and biases, and 4) Ecological rationality. Typically, proponents of 3) and 4) criticize models 1) and 2) for their cognitive unrealism. However, many ethologists contend that it makes sense to account for data gathered in animal behaviour research along the lines defined by the latter models. Elaborating upon this contention, Stanovich suggested recently a fifth standpoint in this debate (...) – I’ll call it ‘Brute Rationality’. According to it, traditional rational choice models are more appropriate to account for the behaviour of creatures endowed with simple cognitive architectures rather than to account for human behaviour. In my talk, I´ll contend that the term ‘rationality’ is being used in this debate to cover too wide a semantic area. In fact, once one distinguishes the different meanings associated with it, the positions defining this debate reveal themselves to be much less clear than what is usually taken to be the case. (shrink)

The paper considers two-agent order acceptance scheduling problems with different scheduling criteria. Two agents have a set of jobs to be processed by a single machine. The processing time and due date of each job are known in advance. In the order accepting scheduling problem, jobs are allowed to be rejected. The objective of the problem is to maximize the net revenue while keeping the weighted number of tardy jobs for the second agent within a predetermined value. A mixed-integer linear (...) programming formulation is provided to obtain the optimal solution. The problem is considered as an NP-hard problem. Therefore, MILP can be used to solve small problem instances optimally. To solve the problem instances with realistic size, heuristic and metaheuristic algorithms have been proposed. A heuristic method is used to determine and secure a quick solution while the metaheuristic based on particle swarm optimization is designed to obtain the near-optimal solution. A numerical experiment is piloted and conducted on the benchmark instances that could be obtained from the literature. The performances of the proposed algorithms are tested through numerical experiments. The proposed PSO can obtain the solution within 0.1% of the optimal solution for problem instances up to 60 jobs. The performance of the proposed PSO is found to be significantly better than the performance of the heuristic. (shrink)

Local deterministic string-to-string transductions arise in natural language processing (NLP) tasks such as letter-to-sound translation or pronunciation modeling. This class of transductions is a simple generalization of morphisms of free monoids; learning local transductions is essentially the same as inference of certain monoid morphisms. However, learning even a highly restricted class of morphisms, the so-called fine morphisms, leads to intractable problems: deciding whether a hypothesized fine morphism is consistent with observations is an NP-complete problem; and maximizing classification accuracy of the (...) even smaller class of alphabetic substitution morphisms is APX-hard. These theoretical results provide some justification for using the kinds of heuristics that are commonly used for this learning task. (shrink)

Leibniz’s Most Determinate Path Principle in Tentamen Anagogicum is an optimization-type law of physics falling into the category of “final cause,” one of “two realms” under discussion there. The other is the “mechanistic/causal.” To be explanatory for Leibniz laws have to be grounded in a causal agency, in the case of the mechanistic realm, the grounding agency is material. I accept, and philosophically defend through a thought experiment, a modern form of this principle, “If a pattern of events is (...) not governed by the laws of chance (are not statistically independent) then there is a causal agency operating to link the events” (The General Version of Leibniz’ Postulate). In Leibniz exegesis, against McDonough, I argue that optimization laws lack an appropriate grounding agency : they are true, predictive and heuristic but not explanatory. So the mechanistic realm is explanatorily fundamental for Leibniz. But Planck maintains the converse: the optimization laws (The Principle of Least Action in his case) are fundamental. (Planck’s Postulate) I defend this postulate against Leibniz and show that if both postulates are right, and a certain plausible condition in theoretical physics holds, the ground for the explanatory power of such laws must lie in Divine Reason. (shrink)

The revelatory paper, “Dilemmas in the General Theory of Planning,” by Rittel and Webber (Policy Sci 4:155–169, 1973 ) has had great impact because it provides one example of an emergent consensus across many disciplines. Many “problems,” as addressed in real-world situations, involve elements that exceed the complexity of any known or hoped-for model, or are “wicked.” Many who encounter this work for the first time find that their concept of wicked problems aptly describes many environmental disputes. For those frustrated (...) with the lack of progress in many areas of environmental protection, Rittel and Webber’s work suggested a powerful explanatory hypothesis: Complex environmental problems cannot be comprehended within any of the accepted disciplinary models available in the academy or in discourses on public interest and policy. What should we conclude about the future of social improvements, and about the possibilities for rational discourse leading to cooperative action, with respect to this huge number of pressing public, environmental problems? Can we find ways to address environmental problems that improves the ability of communities to respond creatively and rationally to them? I will argue that, while the Rittel-Webber critique requires us to abandon many of the assumptions associated with a positivistic view of science and its applications to policy analysis, it also points to a more productive direction for the future of policy analysis. I will introduce “boundary critique,” developed within Critical Systems Theory (CST), an approach that offers some reason for optimism in dealing with some aspects of wickedness. (shrink)

Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is about (...) fast and frugal heuristics--simple rules for making decisions when time is pressing and deep thought an unaffordable luxury. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? Can judgments based simply on one good reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? Simple Heuristics explores these questions, developing computational models of heuristics and testing them through experiments and analyses. It shows how fast and frugal heuristics can produce adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop out rates, and playing the stock market. As an interdisciplinary work that is both useful and engaging, this book will appeal to a wide audience. It is ideal for researchers in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. It will also inspire anyone interested in simply making good decisions. (shrink)

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.

The relations between rationality and optimization have been widely discussed in the wake of Herbert Simon's work, with the common conclusion that the rationality concept does not imply the optimization principle. The paper is partly concerned with adding evidence for this view, but its main, more challenging objective is to question the converse implication from optimization to rationality, which is accepted even by bounded rationality theorists. We discuss three topics in succession: (1) rationally defensible cyclical choices, (2) (...) the revealed preference theory of optimization, and (3) the infinite regress of optimization. We conclude that (1) and (2) provide evidence only for the weak thesis that rationality does not imply optimization. But (3) is seen to deliver a significant argument for the strong thesis that optimization does not imply rationality. (shrink)

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks... it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs." Presenting powerful, proven (...) optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering. (shrink)