This paper investigates the Generalized Traveling Salesman Problem (GTSP), which is an extension of the well-known Traveling Salesman Problem (TSP), and it searches for an optimal tour in a clustered graph, such that every cluster is visited exactly once. In this paper, we describe a novel Memetic Algorithm (MA) for solving efficiently the GTSP. Our proposed MA has at its core a genetic algorithm (GA), completed by a Chromosome Enhancement Procedure (CEP), which is based on a TSP solver and the (...) Shortest Path (SP) algorithm and for improving the convergence characteristics of the GA, a Local Search (LS) operation is applied for the best chromosomes in each generation. We tested our algorithm on a set of well-known instances from the literature and the achieved results prove that our novel memetic algorithm is highly competitive against the existing solution approaches from the specialized literature. (shrink)

Knowing the level of quality from which the context is no longer valuable in a Context-Aware Data Mining (CADM) system is an important information. The main goal of this research is to study the variations of the predictions in case of different levels of noise and missing context data in practical scenarios for predicting soil moisture. The research has been performed on two locations from the Transylvanian Plain, Romania and two locations from Canada. The values predicted for the soil moisture (...) were compared in mixed scenarios that vary the quantity of noise and missing context data. The studied behavior was performed using Deep Learning, Decision Tree and Gradient Boosted Tree machine learning algorithms. It has been shown that when using the air temperature as context for predicting soil moisture, variations of noise and missing data do not influence the results proportionally with the levels of noise and missing data applied. Also, Gradient Boosted Tree algorithm proves to be the best algorithm from the ones studied, to be considered when predicting soil moisture with the CADM approach. (shrink)

In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set of large (...) size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature. (shrink)

A fluent economical collaboration between countries is a major need. European flows of trade and people are supported by efficient connections between main localities from a geographic region, in many cases overriding national borders. This paper introduces three traveling salesmen problem instances based on freely available geographic coordinates of the main cities of France, Portugal and Spain. These instances are unified, generating other four larger instances: three with all pairs of countries and one instance with the settlements from all the (...) three countries. The study includes an analysis of quality of solutions for a version of branch & cut algorithm and some hybrid heuristics including the Lin–Kernighan algorithm. $Bor\mathring{u}vka$, Quick$Bor\mathring{u}vka$ and Greedy algorithms are also used in the hybrid approaches in order to obtain a potential beneficent initial solution for the Lin–Kernighan algorithm. Concorde solver, nowadays state-of-the-art exact software, together with the already mentioned algorithms, is used to test and furthermore analyze the new TSP instances. Some results are represented using online services such as Google Maps, showing the potential integration of the Concorde’s optimum results into commercial routing applications. The very good results provided by the Lin–Kernighan method allow its usage for real medium-sized routing instances. (shrink)

Meeting the security requests in transportation is nowadays a must. The intelligent transport systems represent the support for addressing such a challenge, due to their ability to make real-time adaptive decisions. We propose a new variant of the travelling salesman problem integrating security constraints inspired from ITSs. This optimization problem is called the secure TSP and considers a set of security constraints on its integer variables. Similarities with fuzzy logic are presented alongside the mathematical model of the introduced TSP variant.

Regression testing is applied whenever a code changes, ensuring that the modifications fixed the fault and no other faults are introduced. Due to a large number of test cases to be run, test case prioritization is one of the strategies that allows to run the test cases with the highest fault rate first. The aim of the paper is to present an optimized test case prioritization method inspired by ant colony optimization, test case prioritization–ANT. The criteria used by the optimization (...) algorithm are the number of faults not covered yet by the selected test cases and the sum of severity of the faults. The cost, i.e. time execution, for test cases is considered in the computation of the pheromone deposited on the graph’s edges. The average percentage of fault detected metric, as best selection criterion, is used to uncover maximum faults with the highest severity, and reducing the regression testing time. Several experiments are considered, detailed and discussed, comparing various algorithm parameter’s alternatives. A benchmark project is also used to validate the proposed approach. The obtained results are encouraging, being a cornerstone for new perspectives to be considered. (shrink)