A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization
The Fish School Search (FSS) algorithm is a metaheuristic known for its distinctive exploration and exploitation operators and cumulative success representation approach. Despite its success across various problem domains, the FSS presents issues due to its high number of parameters, making its perf...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/13/5/102 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850258097880170496 |
|---|---|
| author | Elliackin Figueiredo Clodomir Santana Hugo Valadares Siqueira Mariana Macedo Attilio Converti Anu Gokhale Carmelo Bastos-Filho |
| author_facet | Elliackin Figueiredo Clodomir Santana Hugo Valadares Siqueira Mariana Macedo Attilio Converti Anu Gokhale Carmelo Bastos-Filho |
| author_sort | Elliackin Figueiredo |
| collection | DOAJ |
| description | The Fish School Search (FSS) algorithm is a metaheuristic known for its distinctive exploration and exploitation operators and cumulative success representation approach. Despite its success across various problem domains, the FSS presents issues due to its high number of parameters, making its performance susceptible to improper parameterization. Additionally, the interplay between its operators requires a sequential execution in a specific order, requiring two fitness evaluations per iteration for each individual. This operator’s intricacy and the number of fitness evaluations pose the issue of costly fitness functions and inhibit parallelization. To address these challenges, this paper proposes a Simplified Fish School Search (SFSS) algorithm that preserves the core features of the original FSS while redesigning the fish movement operators and introducing a new turbulence mechanism to enhance population diversity and robustness against stagnation. The SFSS also reduces the number of fitness evaluations per iteration and minimizes the algorithm’s parameter set. Computational experiments were conducted using a benchmark suite from the CEC 2017 competition to compare the SFSS with the traditional FSS and five other well-known metaheuristics. The SFSS outperformed the FSS in 84% of the problems and achieved the best results among all algorithms in 10 of the 26 problems. |
| format | Article |
| id | doaj-art-2db2210aeef5420fb86b1a722aba246c |
| institution | OA Journals |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-2db2210aeef5420fb86b1a722aba246c2025-08-20T01:56:16ZengMDPI AGComputation2079-31972025-04-0113510210.3390/computation13050102A Simplified Fish School Search Algorithm for Continuous Single-Objective OptimizationElliackin Figueiredo0Clodomir Santana1Hugo Valadares Siqueira2Mariana Macedo3Attilio Converti4Anu Gokhale5Carmelo Bastos-Filho6Department of Computer Engineering, University of Pernambuco, Recife 50670-901, BrazilDepartment of Internal Medicine, University of California, Davis, CA 95616, USADepartment of Electric Engineering, Federal University of Technology–Paraná, Curitiba 80230-901, BrazilDepartment of Computer Science, Northeastern University London, London E1W 1LP, UKDepartment of Civil, Chemical and Environmental Engineering, Pole of Chemical Engineering, University of Genoa, Via Opera Pia 15, 16145 Genoa, ItalyDepartment of Computer Information Systems, Saint Augustine’s University, Raleigh, NC 27610, USADepartment of Computer Engineering, University of Pernambuco, Recife 50670-901, BrazilThe Fish School Search (FSS) algorithm is a metaheuristic known for its distinctive exploration and exploitation operators and cumulative success representation approach. Despite its success across various problem domains, the FSS presents issues due to its high number of parameters, making its performance susceptible to improper parameterization. Additionally, the interplay between its operators requires a sequential execution in a specific order, requiring two fitness evaluations per iteration for each individual. This operator’s intricacy and the number of fitness evaluations pose the issue of costly fitness functions and inhibit parallelization. To address these challenges, this paper proposes a Simplified Fish School Search (SFSS) algorithm that preserves the core features of the original FSS while redesigning the fish movement operators and introducing a new turbulence mechanism to enhance population diversity and robustness against stagnation. The SFSS also reduces the number of fitness evaluations per iteration and minimizes the algorithm’s parameter set. Computational experiments were conducted using a benchmark suite from the CEC 2017 competition to compare the SFSS with the traditional FSS and five other well-known metaheuristics. The SFSS outperformed the FSS in 84% of the problems and achieved the best results among all algorithms in 10 of the 26 problems.https://www.mdpi.com/2079-3197/13/5/102Simplified Fish School Search (SFSS)swarm intelligencemetaheuristicssingle-objective optimization |
| spellingShingle | Elliackin Figueiredo Clodomir Santana Hugo Valadares Siqueira Mariana Macedo Attilio Converti Anu Gokhale Carmelo Bastos-Filho A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization Computation Simplified Fish School Search (SFSS) swarm intelligence metaheuristics single-objective optimization |
| title | A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization |
| title_full | A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization |
| title_fullStr | A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization |
| title_full_unstemmed | A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization |
| title_short | A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization |
| title_sort | simplified fish school search algorithm for continuous single objective optimization |
| topic | Simplified Fish School Search (SFSS) swarm intelligence metaheuristics single-objective optimization |
| url | https://www.mdpi.com/2079-3197/13/5/102 |
| work_keys_str_mv | AT elliackinfigueiredo asimplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT clodomirsantana asimplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT hugovaladaressiqueira asimplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT marianamacedo asimplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT attilioconverti asimplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT anugokhale asimplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT carmelobastosfilho asimplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT elliackinfigueiredo simplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT clodomirsantana simplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT hugovaladaressiqueira simplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT marianamacedo simplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT attilioconverti simplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT anugokhale simplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization AT carmelobastosfilho simplifiedfishschoolsearchalgorithmforcontinuoussingleobjectiveoptimization |