Multi-Swarm Particle Swarm Optimization Using Opposition-Based Learning and Application in Coverage Optimization of Wireless Sensor Network

Particle swarm optimization (PSO) has been shown that it can yield good performance for solving some optimization problems. However, it converges slowly at the later stage with low precision. This paper presents an effective approach, called Multi-swarm Particle Swarm Optimization using Opposition-Based Learning (OLMPSO), which divides swarm into 2 sub-swarms. The 1st subswarm employs PSO evolution model in order to hold the self-learning ability; the opposite solution of particle and the optimum between two sub-swarms are introduced into the 2nd sub-swarm which adopts new evolution model with boosting self-escaping and society learning ability of particle. The new method can enhance the diversity of swarm and improve the ability of escaping local optimum. And we apply it into coverage optimization of wireless sensor network, and the simulation results showed that the proposed approach gets better coverage.