Particle Swarm Optimization with Hybrid Velocity Updating Strategies

摘要

Particle Swarm Optimization (PSO) is a recently proposed population-based evolutionary algorithm, which shows good performance in many optimization problems. To achieve better performance, this paper presents a new variant of PSO algorithm called PSO with Hybrid Velocity Updating Strategies (HVS-PSO). HVS-PSO employs another two velocity updating strategies besides the original velocity updating strategy. Experimental studies on six well-known benchmark problems show that HVS-PSO outperforms PSO with inertia weight (PSO-w), local version of PSO with inertia weight (PSO-w-local), and fully informed particle swarm (FIPS) on majority of test problems