Gonzalo Nápoles1, Isel Grau2, and Rafael Bello1
1 Laboratory of Artificial Intelligence, Universidad Central Marta Abreu de Las Villas (UCLV), Santa Clara, Cuba, (e–mail: gnapoles@uclv.edu.cu, rbellop@uclv.edu.cu).
2 Laboratory of Bioinformatics, Universidad Central Marta Abreu de Las Villas (UCLV), Santa Clara, Cuba, (e–mail: igrau@uclv.edu.cu).
Manuscript received June 20, 2012.
Manuscript accepted for publication July 24, 2012.
]]> Abstract
Particle Swarm Optimization (PSO) is a bioinspired meta–heuristic for solving complex global optimization problems. In standard PSO, the particle swarm frequently gets attracted by suboptimal solutions, causing premature convergence of the algorithm and swarm stagnation. Once the particles have been attracted to a local optimum, they continue the search process within a minuscule region of the solution space, and escaping from this local optimum may be difficult. This paper presents a modified variant of constricted PSO that uses random samples in variable neighborhoods for dispersing the swarm whenever a premature convergence (or stagnation) state is detected, offering an escaping alternative from local optima. The performance of the proposed algorithm is discussed and experimental results show its ability to approximate to the global minimum in each of the nine well–known studied benchmark functions.
Key words: Particle Swarm Optimization, Local optima, Global Optimization, Premature Convergence, Random Samples, Variable Neighborhoods.
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