Keywords
Metaheurísticas
Metaheurísticas basadas en trayectoria Multiobjective optimization
Metaheuristics
Trajectory-based metaheuristics
How to Cite
Abstract
In this paper, we introduce a Local Search-based multiobjective algorithm proposal (MOLS) and we have carried out a comparative study of its performance with other two classical multiobjective algorithms: Pareto Archived Evolution Strategy (PAES) and NonDominated Sorting Genetic Algorithm-II (NSGA-II), chosen by, first metaheuristic because applies Local Search procedure similar to MOLS and second metaheuristic because is the main multiobjective reference.
This study is carried out with the aim to know how competitive are the trajectory-based metaheuristics solving NP-hard MultiObjective Optimization Problems (MOPs). The performance of these algorithms is evaluated by solving the problems of the benchmark ZDT Test Suite.
For the analysis of the results, three multiobjective quality indicators are considered: Epsilon, Spread and hypervolume. The obtained results indicate that the proposed algorithm MOLS is able to find a better spread of solutions and a better convergence to the true Pareto-optimal front in comparison to the results generated by NSGAII and PAES.
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Copyright (c) 2019 Joel Alberto Cedeño Muñoz