Improved particle swarm optimization based on Hénon chaos and dynamic nonlinear equations
基于Hénon混沌与动态非线性方程的改进粒子群优化算法*

To solve the premature problem of particle swarm optimization, introduced two new methods to improve particle swarm optimization: when the fitness values of some particles were worse than the average, devised the dynamic Hénon chaotic map formula to modify the inertia weight, which could make particles break away from the local optima and search the global optima dynamically. On the contrary, when the fitness values of some particles were better than or equal to the average, employed the new introduced dynamic nonlinear equations to modify the inertia weight, which could retain favorable conditions and converge to the global optima continually. Two methods coordinated with each other dynamically, and made two dynamic swarms cooperate to evolve. Some well-known benchmark functions with different complexities were employed to test the performance of the new introduced algorithm. Experimental results demonstrate that the new introduced methods outperformed several other famous improved particle swarm optimization algorithms in different situations.