协同粒子群优化算法

为解决粒子群优化算法易陷入局部最优的问题，提出了两种新方法协同处理粒子群优化算法：对比平均适应度值差的粒子，用动态Zaslavskii混沌映射公式改进粒子惯性权重与速度矢量，在复杂多变的环境中逐步摆脱局部最优值，动态寻找全局最优值；对好于或等于适应度平均值的粒子，用动态非线性函数调整粒子惯性权重与速度矢量，在保存相对有利环境的基础上逐步向全局最优处收敛。两种方法相辅相成、动态协调，使两个动态种群相互协作、协同进化。实验表明该算法在多个标准测试函数下都超越了同类著名改进算法。

Cooperative particle swarm optimization

To solve the premature convergence problem of Particle Swarm Optimization (PSO), two new methods were introduced to improve the performance cooperatively: When particles' fitness values were worse than the average, the dynamic Zaslavskii chaotic map formula was devised to modify the inertia weight and velocity, which can make particles break away from the local best and search the global best dynamically; On the contrary, when fitness values were better than or equal to the average, the introduced dynamic nonlinear functions were used to modify the inertia weight and velocity, which can make particles retain favorable conditions and converge to the global best continually. Two methods coordinate dynamically, and make two dynamic swarms cooperate to evolve. Experimental results demonstrate that the new introduced algorithm outperforms several other famous improved PSO algorithms on many well-known benchmark problems.