Multi-objective evolutionary algorithms for a reliability location problem

Alcaraz, Javier; Landete, Mercedes; Monge, Juan F.; Sainz-Pardo, José L.

doi:10.1016/j.ejor.2019.10.043

Cited by 16 publications

(5 citation statements)

References 27 publications

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“…In COPs' literature, many approximation algorithms are used for solving the MOCOPs, such as, multi-objective discrete artificial bee colony (ABC) [22], [23], [24], [25], [26], [27]; multi-objective ant colony optimization (MOACO) [28]; improved artificial immune algorithm [29]; MOEA/D [30]; multi-objective memetic algorithm [31], [32], [33], [34]; water wave optimization [35]; modified particle swarm optimization (PSO) [36], [37]; multi-objective hybrid immune algorithm [38]; GA [39]; grey wolf optimization [40]; cooperative swarm intelligence algorithm for MODOP [41]; multi-objective fruit fly optimization algorithm [42]; multi-objective discrete virus optimization algorithm [43]; NSGA-II & SPEA-II [44] and subpopulation based multiobjective evolutionary algorithm [45]. As this study is focussed on reviewing NSGA-II for MOCOPs, a detailed view of NSGA-II implementations for selected MOCOPs is given in next sub-sections.…”

Section: Review Of Literaturementioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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Section: Review Of Literaturementioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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“…The result of the insertion operator is the best of the evaluated solutions. This operator is based on the idea of the "adding" procedure used in the metaheuristic proposed in Alcaraz et al (2019).…”

Section: Insertionmentioning

confidence: 99%

The linear ordering problem with clusters: a new partial ranking

Alcaraz

García-Nové

Landete

et al. 2020

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The linear ordering problem is among core problems in combinatorial optimization. There is a squared non-negative matrix and the goal is to find the permutation of rows and columns which maximizes the sum of superdiagonal values. In this paper, we consider that columns of the matrix belong to different clusters and that the goal is to order the clusters. We introduce a new approach for the case when exactly one representative is chosen from each cluster. The new problem is called the linear ordering problem with clusters and consists of both choosing a representative for each cluster and a permutation of these representatives, so that the sum of superdiagonal values of the sub-matrix induced by the representatives is maximized. A combinatorial linear model for the linear ordering problem with clusters is given, and eventually, a hybrid metaheuristic is carefully designed and developed. Computational results illustrate the performance of the model as well as the effectiveness of the metaheuristic.

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“…Another study 12 showed that nearly 78% of the studies on switchgrass-based biofuel supply chains were modelled on an economic objective. Several methods could be used for multi-objective optimization (MOO); 13 among them, the weighting method and the ε-constraint are the most widely used methods. 14 The ε-constraint is the most well-known method because it is rigorous for both convex and nonconvex problems.…”

Section: Introductionmentioning

confidence: 99%