求解高维优化问题的扰动混沌蚁群优化算法

摘要

针对新型混沌蚁群优化算法(CAS)求解高维优化问题时存在的计算复杂和搜索精度低问题,提出了扰动混沌蚂蚁群(DCAS)算法.通过建立蚂蚁最佳位置更新贪婪规则和随机邻居选择方法有效地降低了计算复杂度;另外引入自适应扰动策略改进CAS算法,使蚂蚁增强局部搜索能力,提高了原算法的搜索精度.通过一组高维测试函数对DCAS算法的性能进行了高达1 000维的仿真实验.测试结果表明,新算法对复杂的高维优化问题可行有效.%To resolve the problems of computational complexity and search precision existing in Chaotic Ant Swarm (CAS), a Disturbance CAS (DCAS) algorithm was proposed to significantly improve the performance of the original algorithm.DCAS algorithm reduced computational complexity by a new greedy method of updating ant's best position and a random neighbor selection method. Furthermore, a serf-adaptive disturbance strategy was introduced to improve the precision of DCAS by developing ant's local search. Extensive computational studies were also earriod out to evaluate the performance of DCAS on a new suite of benchmark functions with up to 1 000 dimensions. The results show clearly that the proposed algorithm is effective as well as efficient for the complex high-dimensional optimization problems.