基于Tent混沌序列的灰狼优化算法

Grey Wolf Optimizer Based on Tent Chaotic Sequence

针对灰狼优化(GWO)算法在优化求解过程中存在的局部搜索能力差、易陷入局部最优等问题,提出了基于Tent混沌序列的局部搜索策略以及多样性维持策略.由此构建了基于Tent混沌序列的灰狼优化算法(TCGWO).所提出的策略分别用以提高算法的局部搜索能力、避免算法陷入局部最优.通过对23个测试函数的仿真计算,并与GWO以及FPA、ABC等进行综合对比,结果验证了TCGWO算法的有效性及优越性.

Focusing on the disadvantages that Grey Wolf Optimizer（GWO）was easy to fall into local optimum and had bad local searching ability on optimization,local search strategy and diversity maintenance strategy were proposed.On the basis of that,the Grey Wolf Optimizer Based on Tent Chaotic Sequence（TCGWO）was established.The local searching ability could be improved by local search strategy;falling into local optimum could be avoided by diversity maintenance strategy.23 benchmark test functions were used to experiment and analysis,and comparative analysis with GWO,Flower Pollination Algorithm（FPA）and Artificial Bee Colony Algorithm（ABC）.According to the result,TCGWO is effective and superior.