Self-Adaptive Crossover Particle Swarm Optimizer for Multi-dimension Functions Optimization

Authors:
Dongyong Yang;Jinyin Chen;Matsumoto Naofumi
Affiliations:
Zhejiang University of Technology, China;Zhejiang University of Technology, China;Ashikaga Institute of Technology, Japan
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Year:
2007

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Discrete optimization problem solving with three variants of hybrid binary particle swarm optimization

Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems

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Abstract

Based on analyzing that solution diversity can be improved by bringing crossover operation into particle swarm optimization, crossover particle swarm optimizer is put forward and applied to optimize multi-dimension benchmark functions. Outcomes testify that Crossover PSO can achieve better performances than other current mended PSOs, and cost less CPU time. Four self-adaptive probability models are adopted to adjust the crossover probability based on particle swarm optimization convergence model. Results and convergence rate of the four models are compared and analyzed finally.