An Adaptive Multi-Crossover Population Algorithm for Solving Routing Problems

Osaba, Eneko; Onieva, Enrique; Carballedo, Roberto; Díaz, Fernando; Perallos, Asier

doi:10.1007/978-3-319-01692-4_9

Cited by 21 publications

(19 citation statements)

References 36 publications

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“…It is logical to think that using this operator the GA will get good results for the TSP, as the VGX makes by itself a small optimization on the resulting individuals. If the objective is to implement a metaheuristic, operators like Order Crossover [63], Half Crossover [64], Order Based Crossover [65] or Modified Order Crossover [66] should be chosen as crossover function, since they are neutral operators. These functions, also known as Blind Crossover Operators, only care to meet the constraints of the problem and they do not use any kind of information related to the problem.…”

Section: Good Practices About the Implementation And Presentation Of mentioning

confidence: 99%

Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

et al. 2018

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Researchers who investigate in any area related to computational algorithms (both defining new algorithms or improving existing ones) usually find large difficulties to test their work. Comparisons among different researches in this field are often a hard task, due to the ambiguity or lack of detail in the presentation of the work and its results. On many occasions, the replication of the work conducted by other researchers is required, which leads to a waste of time and a delay in the research advances. The authors of this study propose a procedure to introduce new techniques and their results in the field of routing problems. In this paper this procedure is detailed, and a set of good practices to follow are deeply described. It is noteworthy that this procedure can be applied to any combinatorial optimization problem. Anyway, the literature of this study is focused on routing problems. This field has been chosen because of its importance in real world, and its relevance in the actual literature.

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Section: Good Practices About the Implementation And Presentation Of mentioning

confidence: 99%

Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

et al. 2018

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“…The metaheuristic used is an Adaptive Multi-Crossover Evolutionary Algorithm (AMCEA). This algorithm was first introduced in [27], proving to be a competitive alternative to solve routing problems, such as the traveling salesman problem, the capacitated vehicle routing problem, vehicle routing with backhauls and multi-depot vehicle routing problem.…”

Section: B Proposed Technique For the Dac-vrp-vsttmentioning

confidence: 99%

Improvement of Drug Delivery Routes Through the Adoption of Multi-Operator Evolutionary Algorithms and Intelligent Vans Capable of Reporting Real-Time Incidents

Onieva

Osaba

Angulo

et al. 2017

IEEE Trans. Automat. Sci. Eng.

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An improved solution for drug distribution is presented in this paper. It is divided into two parts: i) a multi-operator evolutionary algorithm in charge of calculating the initial delivery routes and ii) an ambient intelligence-based support system able to tracing the merchandise along the distribution route. The first one establishes the routes to be followed by the vehicles, based on a proposal of estimation of the travel times. The second one is formed by a system able to recognize and trace the drugs inside each vehicle. A laboratory experimentation has been conducted in order to demonstrate the adequacy of the route calculator. In addition, a field experimentation has been carried out by implementing the traceability system in a delivery van which of the drug distributor in the city of Bilbao.Note to Practitioners-Motivated by the specific needs found by a drug distributor in managing deliveries to pharmacies involved in its supply chain, this paper considers an optimization problem as the proper way to model the particularities found in this scenario. This paper proposes the use of a combinatorial optimization problem as the way to model problems. Due to the fact that distributor's clients are located in different cities, the proposal considers travel times both between cities and inside the city. In addition, rush hours are also taken into account, in order to provide a more realistic model. Finally, a radio frequency identification-based solution is proposed, in order to implement traceability in the delivered products, as well as to prevent human errors. Additionally, in case an unavoidable error occurs, the solution offers also an incident management mechanism.

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“…Because of its easy implementation and its good performance, this function is often used in the literature for any kind of permutation encoded problem [84, 85]. …”

Section: Experimentation Setupmentioning

confidence: 99%

Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

Osaba

Díaz

Carballedo

et al. 2014

The Scientific World Journal

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Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

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An Adaptive Multi-Crossover Population Algorithm for Solving Routing Problems

Cited by 21 publications

References 36 publications

Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

Improvement of Drug Delivery Routes Through the Adoption of Multi-Operator Evolutionary Algorithms and Intelligent Vans Capable of Reporting Real-Time Incidents

Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

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