Harnessing Particle Swarm Optimization Through Relativistic Velocity

• Autor(a) principal: Roder, Mateus [UNESP]
• Data de Publicação: 2020
• Outros Autores: Rosa, Gustavo Henrique de [UNESP], Passos, Leandro Aparecido [UNESP], Papa, Joao Paulo [UNESP], Debiaso Rossi, Andre Luis [UNESP], IEEE
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/218679
• topic: Global Optimization; Meta-Heuristic Optimization; Particle Swarm Optimization; Theory of Relativity; Relativistic Particle Swarm Optimization

In the last century, Albert Einstein's perceptions of the world afforded a revolution in the understanding of the universe. In his theory of general relativity, he describes the space-time continuum, a concept capable of explaining several phenomena, ranging from gravity to black holes and supernovas. Further, it also provides a set of formulations to generalize classical physics concepts to accommodate the relativistic notions. Meanwhile, several mathematicians have been working on optimization tools aiming to solve complex problems associated with a large number of variables. Nowadays, despite the computational power, many daily tasks still pose a challenge and are becoming more prohibitives, mostly due to the massive amount of data to be processed. Therefore, efficient optimization techniques are more desirable than ever. In this context, meta-heuristic optimization has arisen, i.e., stochastic nature-inspired methods capable of finding sub-optimal solutions for complex problems with a reasonable computational effort. However, such approaches still suffer from some drawbacks related to low convergence and getting stuck on local optima, among others. Therefore, in this paper, we introduce relativistic concepts into the well-known meta-heuristic optimization technique Particle Swarm Optimization (PSO). The experimental results evince the robustness of the proposed approach compared to the standard PSO as well as three other variations for five benchmarking functions.