摘要： 将边界变异操作引入到量子粒子群优化算法中，提出基于边界变异的量子粒子群优化算法QPSOB。该算法将越界粒子随机分布在边界附近的可行域内，以增加种群的多样性、提高算法的全局搜索能力。仿真实验证明其全局收敛性能优于量子粒子群优化算法。

关键词: 边界变异, 多样性, 量子粒子群优化算法

Abstract: This paper introduces a bounded mutation operator into Quantum-behaved Particle Swarm Optimization(QPSO) algorithm and proposes QPSOB. It distributes the particle randomly beyond the boundary around the boundary. The modified algorithm increases diversity of population, and improves global searching ability. Experimental results show that QPSOB has stronger global convergence ability than QPSO.

Key words: bounded mutation, diversity, Quantum-behaved Particle Swarm Optimization(QPSO) algorithm

中图分类号: 

• TP301.6