Abstract
Particle swarm optimization (PSO) is a novel swarm intelligent algorithm inspired by fish schooling and birds flocking. Due to the complex nature of engineering optimization tasks, the standard version can not always meet the optimization requirements. Therefore, in this paper, a new group decision mechanism is introduced to PSO to enhance the escaping capability from local optimum. Furthermore, a Watts Strogatz small-world model is incorporated into PSO to increase the population diversity.Seven famous numerical benchmarks are used to testify the new algorithm. Simulation results show it achieves the best performance when compared with three other variants of particle swarm optimization especially for multi-modal problems.
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