Abstract
The following paper presents a novel strategy named Maximum Search Limitations (MS) for the Evolutionary Particle Swarm Optimization (EPSO). The approach combines EPSO standard search mechanism with a set of rules and position-wise statistics, allowing candidate solutions to carry a more thorough search around the neighborhood of the best particle found in the swarm. The union of both techniques results in an EPSO variant named MS-EPSO. MS-EPSO crucial premise is to enhance the exploration phase while maintaining the exploitation potential of EPSO. Algorithm performance is measured on eight unconstrained and two constrained engineering design optimization problems. Simulations are made and its results are compared against other techniques including the classic Particle Swarm Optimization (PSO). Lastly, results suggest that MS-EPSO can be a rival to other optimization methods.
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This paper was produced under conditions provided by funding by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization - COMPETE 2020 within project POCI-01-0145-FEDER-006961, and by national funds through the FCT – Portuguese Foundation for Science and Technology, as part of project UID/EEA/ 50014/2013
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Neto, M.S., Mollinetti, M., Miranda, V., Carvalho, L. (2019). Maximum Search Limitations: Boosting Evolutionary Particle Swarm Optimization Exploration. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_59
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