A Novel Hybrid Particle Swarm Optimization Algorithm

Abstract:

Particle swarm optimization (PSO) is a global algorithm which is inspired by birds flocking and fish schooling. PSO has shown good search ability in many complex optimization problems, but premature convergence is still a main problem. A novel hybrid PSO(NHPSO) was proposed, which employed hybrid strategies, including dynamic step length (DSL) and opposition-based learning (OBL). DSL is helpful to enhance local search ability of PSO, and OBL is beneficial for improving the quality of candidate solutions. In order to verify the performance of NHPSO, we test it on several benchmark functions. The simulation results demonstrate the effectiveness and efficiency of our approach.

• Periodical: Applied Mechanics and Materials (Volumes 409-410)
• Pages: 1611-1614
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.409-410.1611
• Online since: September 2013
• Authors: Lei Chen
• Keywords: Dynamic Step Length, Opposition-Based Learning, Particle Swarm Optimization (PSO)

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