Particle swarm optimization with a novel mutation operator

Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems, it easily falls into local optima when solving complex multimodal problems. This paper aims to enhance the performance of PSO in complex optimization problems and proposes an improved PSO variant by incorporating a novel mutation operator. Experimental studies on some well-known benchmark problems show that our approach achieves promising results.