Step-Size Individualization: a Case Study for The Fish School Search Family

This study proposes a new strategy to improve the performance of the algorithms of the Fish School Search (FSS) family via the individualization of the step-size of each fish. We propose to be calculated in two different manners: using individual weight or using individual fitness, depending on the...

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Bibliographic Details
Published in2022 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors de Andrade Amorim Neto, Hugo, de Lacerda, Marcelo Gomes Pereira, de Lima Neto, Fernando Buarque
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.07.2022
Subjects
Benchmark testing
Evolutionary computation
Fish
Fish School Search
Genetics
Metaheuristic
Population-based algorithms
Search problems
self-adapting parameters
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Summary:This study proposes a new strategy to improve the performance of the algorithms of the Fish School Search (FSS) family via the individualization of the step-size of each fish. We propose to be calculated in two different manners: using individual weight or using individual fitness, depending on the chosen variation of the proposed technique. Our methods were tested on the original FSS, on the Weight based Fish School Search (wFSS) and on the Multi Objective Fish School Search (MOFSS) algorithms. The benchmark functions of the Congress on Evolutionary Computation, The Genetic and Evolutionary Computation Conference (CEC'2020, CEC'2013, and GECCO'2016) and the DTLZ test suite were used to assess the experimental results, which yielded that all variants of the FSS algorithm tested have been improved in the majority of the scenarios.
DOI:10.1109/CEC55065.2022.9870212