基于混沌序列的自适应粒子群优化算法

提出一种改进粒子群局部搜索能力的自适应优化算法。通过大量仿真试验，考察粒子平均速度和收敛性之间的关系，给出一种新的自适应调整权重策略。以粒子平均速度作为反馈信息，动态调整权重因子，控制粒子速度并使其沿理想速度曲线下降。在搜索过程中引入混沌序列以改进算法的局部搜索能力。对经典函数的测试结果表明，改进的混合算法通过微粒自适应更新机制确保了全局搜索性能和局部搜索性能的动态平衡，在稳定性和精度上均优于普通PSO算法。

Adapting Particle Swarm Optimization Algorithm Based on Chaotic Series

This paper proposes an improved adapting algorithm to enhance the local search ability of particle swarm. The relationship between swarm average velocity and convergence is studied through numerical simulations. A novel inertia weight adjusting method is proposed. In this algorithm, inertia weight is adjusted dynamically according to the absolute average velocity which is the feedback information, and the average velocity is controlled to follow a given ideal velocity curve. Chaotic series is introduced in search to improve the local search ability of the algorithm. The experimental results of classic functions show that the improved hybrid method keeps the balance between the global search and the local search, and has great advantage of convergence property and robustness compared with PSO algorithm.