Fish School Search Algorithm for Constrained Optimization

In this work we investigate the effectiveness of the application of niching able swarm metaheuristic approaches in order to solve constrained optimization problems. Sub-swarms are used in order to allow the achievement of many feasible regions to be exploited in terms of fitness function. The nichin...

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Bibliographic Details
Published in:arXiv.org 2017-07
Main Authors: Joao Batista Monteiro Filho, Isabela Maria Carneiro de Albuquerque, Fernando Buarque de Lima Neto
Format: Article
Language:English
Subjects:
Fitness
Heuristic methods
Operators (mathematics)
Optimization
Search algorithms
Online Access:Get full text
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Summary:In this work we investigate the effectiveness of the application of niching able swarm metaheuristic approaches in order to solve constrained optimization problems. Sub-swarms are used in order to allow the achievement of many feasible regions to be exploited in terms of fitness function. The niching approach employed was wFSS, a version of the Fish School Search algorithm devised specifically to deal with multi-modal search spaces. A base technique referred as wrFSS was conceived and three variations applying different constraint handling procedures were also proposed. Tests were performed in seven problems from CEC 2010 and a comparison with other approaches was carried out. Results show that the search strategy proposed is able to handle some heavily constrained problems and achieve results comparable to the state-of-the-art algorithms. However, we also observed that the local search operator present in wFSS and inherited by wrFSS makes the fitness convergence difficult when the feasible region presents some specific geometrical features.
ISSN:2331-8422