{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:12:52Z","timestamp":1760058772253,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/computation13050102","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T04:08:24Z","timestamp":1745554104000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2540-993X","authenticated-orcid":false,"given":"Elliackin","family":"Figueiredo","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, University of Pernambuco, Recife 50670-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7869-7184","authenticated-orcid":false,"given":"Clodomir","family":"Santana","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, University of California, Davis, CA 95616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1278-4602","authenticated-orcid":false,"given":"Hugo Valadares","family":"Siqueira","sequence":"additional","affiliation":[{"name":"Department of Electric Engineering, Federal University of Technology\u2013Paran\u00e1, Curitiba 80230-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7071-379X","authenticated-orcid":false,"given":"Mariana","family":"Macedo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Northeastern University London, London E1W 1LP, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2488-6080","authenticated-orcid":false,"given":"Attilio","family":"Converti","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical and Environmental Engineering, Pole of Chemical Engineering, University of Genoa, Via Opera Pia 15, 16145 Genoa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2955-1589","authenticated-orcid":false,"given":"Anu","family":"Gokhale","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Saint Augustine\u2019s University, Raleigh, NC 27610, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0924-5341","authenticated-orcid":false,"given":"Carmelo","family":"Bastos-Filho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Pernambuco, Recife 50670-901, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. Am."},{"key":"ref_2","unstructured":"Eberhart, R., and Kennedy, J. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","article-title":"A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm","volume":"39","author":"Karaboga","year":"2007","journal-title":"J. Glob. Optim."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bastos-Filho, C.J.A., de Lima-Neto, F.B., Lins, A.J.D.C.C., de Lacerda, M.G.P., da Motta Macedo, M.G., de Santana Junior, C.J., Siqueira, H.V., da Silva, R.C.L., Neto, H.A., and de Melo Menezes, B.A. (2021). Fish School Search: Account for the First Decade. Handbook of AI-Based Metaheuristics, CRC Press.","DOI":"10.1201\/9781003162841-3"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0304-3975(00)00406-0","article-title":"Theory of genetic algorithms","volume":"259","author":"Schmitt","year":"2001","journal-title":"Theor. Comput. Sci."},{"key":"ref_6","first-page":"1","article-title":"Encoding schemes in genetic algorithm","volume":"2","author":"Kumar","year":"2013","journal-title":"Int. J. Adv. Res. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1177\/1748301816665021","article-title":"A review of velocity-type pso variants","volume":"11","author":"Sousa","year":"2017","journal-title":"J. Algorithms Comput. Technol."},{"key":"ref_8","unstructured":"Filho, C.J.B., Neto, F.B.d., Lins, A.J., Nascimento, A.I., and Lima, M.P. (2008, January 12\u201315). A novel search algorithm based on fish school behavior. Proceedings of the 2008 IEEE International Conference on Systems, Man and Cybernetics, Singapore."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"de Albuquerque, I.M., Filho, J.M., Neto, F.B.d.L., and Silva, A.M.d.O. (2016, January 6\u20139). Solving assembly line balancing problems with fish school search algorithm. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7849991"},{"key":"ref_10","unstructured":"Ronald, S. (1997, January 13\u201316). Robust encodings in genetic algorithms: A survey of encoding issues. Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (ICEC\u201997), Indianapolis, IN, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.asoc.2014.08.025","article-title":"A comparative review of approaches to prevent premature convergence in GA","volume":"24","author":"Pandey","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.asoc.2014.10.026","article-title":"Enhanced leader pso (elpso): A new pso variant for solving global optimisation problems","volume":"26","author":"Jordehi","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1080\/08839514.2018.1481903","article-title":"An improved pso algorithm with genetic and neighborhood-based diversity operators for the job shop scheduling problem","volume":"32","year":"2018","journal-title":"Appl. Artif. Intell."},{"key":"ref_14","unstructured":"Tang, Q., Zeng, J., Li, H., Li, C., and Liu, Y. (2009, January 26\u201329). A particle swarm optimization algorithm based on genetic selection strategy. Proceedings of the Advances in Neural Networks\u2013ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, China. Proceedings, Part III 6."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16239","DOI":"10.1007\/s00521-022-06981-4","article-title":"Artificial bee colony algorithm with an adaptive search manner and dimension perturbation","volume":"34","author":"Ye","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4935","DOI":"10.3906\/elk-1404-45","article-title":"Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms","volume":"24","author":"Makas","year":"2016","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s00500-016-2334-4","article-title":"Artificial bee colony algorithm with an adaptive greedy position update strategy","volume":"22","author":"Yu","year":"2018","journal-title":"Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Knypi\u0144ski, \u0141., Kurzawa, M., Wojciechowski, R., and Gw\u00f3\u017ad\u017a, M. (2024). Application of the Salp Swarm Algorithm to Optimal Design of Tuned Inductive Choke. Energies, 17.","DOI":"10.3390\/en17205129"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khajehzadeh, M., Iraji, A., Majdi, A., Keawsawasvong, S., and Nehdi, M.L. (2022). Adaptive Salp Swarm Algorithm for Optimization of Geotechnical Structures. Appl. Sci., 12.","DOI":"10.3390\/app12136749"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.1007\/s00500-018-3206-x","article-title":"Domination landscape in evolutionary algorithms and its applications","volume":"23","author":"Hao","year":"2019","journal-title":"Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Santana, C.J., Bastos-Filho, C.J., Macedo, M., and Siqueira, H. (2019, January 10\u201313). Sbfss: Simplified binary fish school search. Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand.","DOI":"10.1109\/CEC.2019.8789973"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.procs.2021.04.156","article-title":"Application of chaotic Fish School Search optimization algorithm with exponential step decay in neural network loss function optimization","volume":"186","author":"Demidova","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","unstructured":"Wu, G., Mallipeddi, R., and Suganthan, P. (2016). Problem definitions and evaluation criteria for the cec 2017 competition and special session on constrained single objective real-parameter optimization. Nanyang Technol. Univ. Singap. Tech. Rep., 1\u201318."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/5\/102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:21:13Z","timestamp":1760030473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/5\/102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":23,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["computation13050102"],"URL":"https:\/\/doi.org\/10.3390\/computation13050102","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2025,4,25]]}}}