融合单纯形法和个体记忆的鲸鱼算法及工程应用

Whale optimization algorithm integrating simplx method and individual memory and its engineering application

针对传统鲸鱼优化算法寻优精度低、收敛速度慢、易获局部最优的不足,提出基于单纯形法和融入个体记忆的改进鲸鱼优化算法。引入非线性收敛因子调整机制,使收敛因子呈现不同递减速率,前期注重全局搜索,后期注重精细开发,协调搜索与开发的平滑转换;引入单纯形法增强种群局部搜索能力,提升寻优收敛速度;融合个体记忆和种群最优解改善位置更新,协调个体与种群的信息交流和个体记忆对算法的搜索和寻优能力。基准函数寻优测试结果表明,该算法可以有效提升收敛速度和寻优精度,避免局部最优解。将改进算法应用于拉伸弹簧设计这类典型工程设计问题,验证了算法的可行性。

Traditional whale optimization algorithm exists some shortages,such as lower optimization precision,slower convergence and easiness to get a local optimum.An improved whale optimization algorithm integrating simplex method and individual memory was presented.A non-linear adjustment mechanism for convergence factor was introduced.The improved convergence factor presented different depletion rates in the early and later iteration.The early moment focused on the global search and the later moment focused the exquisite exploitation,which realized a smooth transition between the search and the exploitation.A simplex method was introduced to promote the local search ability of population and improve the convergence speed.The indivi-dual memory and the population optimum were fused to improve the position update,the information exchange of the individual and population and the search and optimization ability of individual memory for the algorithm was coordinated on the whole.The benchmark functions optimization test was constructed.Its results show that the proposed algorithm can effectively improve the convergence speed and the searching precision,and avoid the local optimum.The typical engineering design problems such as extension spring design were used to verify the feasibility of the algorithm.