几种混沌布谷鸟搜索算法的优化性能比较与仿真

Performance comparison and stimulation of chaotic cuckoo search algorithms

针对布谷鸟搜索迭代后期收敛速度慢和搜索精度不高的不足,通过将混沌优化方法嵌入到布谷鸟搜索中构建混沌布谷鸟搜索算法。新算法利用混沌序列的随机性和遍历性来改善布谷鸟搜索的优化性能。基准测试函数集的测试结果显示新算法在收敛速度和计算精度方面都得以提高。最后将所构建的算法应用到PID参数整定问题上,对比实验结果表明改进算法是可行性和有效性的。

Based on chaos optimization algorithm,a series of chaotic cuckoo search algorithms are presented to enhance the convergence rate of the last iteration and the accuracy of cuckoo search.The performance of cuckoo search algorithm is improved by the randomness and ergodicity of chaotic sequence.And test results of benchmark functions show new algorithms obtain good performance of convergence rate and accuracy.Finally,these modified algorithms were used to solve the PID controller tuning problem and simulation results demonstrate the feasibility and effectiveness of the proposed algorithms.