关键词: 粒子群优化算法, 加速系数, 个体最优位置

Abstract: On the basis of particle swarm optimization, An extended particle swarm optimization is first presented by replacing personal best particle with the average of personal best particles in swarm. Then, An adaptive acceleration coefficients extended particle swarm optimization is proposed by establishing a nonlinear functional relationship between acceleration coefficients and the different of the average fitness of all particles and the fitness global best particle. The proposed algorithms apply more particles’ information, and adjust adaptively “cognition” component and “social” component by nonlinear time-varying acceleration coefficients, thus improves convergence performance. The experiment results demonstrate that the proposed algorithms are superior to original particle swarm optimization algorithm.

Key words: Particle swarm optimization, Acceleration coefficient, Personal best particle