动态环境下基于种群多样性的微粒群算法

针对现有环境检测和环境响应方法存在的不足,提出了改进的基于微粒自身信息的检测方法,不仅降低了的算法复杂度,而且弥补了常用检测方法的局限性。同时还提出了种群多样性和微粒逃逸行为相结合的新型响应方法,将改进的检测和响应方法应用于各种复杂变化的抛物线函数中,结果表明该算法在动态环境中的有效性。

Particle Swarm Optimizer Based on Diversity of Particle in Dynamic Environments

To the shortages existing among the current environmental detection and response techniques,an improved detection method based on the particle information was developed,which not only could reduce the optimization cost but also could make up the limitation of the usual detection methods.At the same time,a new response method combined with population diversity and escaping behavior was designed.The improved detection and response algorithms were applied to solve various parabola functions with complex dynamic changes,and the simulation results show the proposed algorithm is effective in dynamic environments.