基于疯狂自适应的樽海鞘群算法

Salp swarm algorithm based on craziness and adaptive

针对樽海鞘群算法求解精度不高和收敛速度慢等缺点,提出一种基于疯狂自适应的樽海鞘群算法.引入Tent混沌序列生成初始种群,以增加初始个体的多样性;在食物源位置上引入疯狂算子,增强种群的多样性;在追随者位置更新公式中引入自适应惯性权重,使算法的全局搜索和局部搜索能力得到更好的平衡.使用统计分析、收敛速度分析、Wilcoxon检验、经典基准函数和CEC2014函数的标准差评估改进樽海鞘群算法的效率.结果表明,改进算法具有更好的全局搜索能力和求解鲁棒性,同时,寻优精度和收敛速度也比原来算法有所增强,尤其在求解高维和多峰测试函数上,改进算法拥有更好的性能.

In order to solve the problem of that the standard salp swarm algorithm(SSA) has slow convergence velocity and low result precision in the evolutionary process, an improved algorithm, called crazy and adaptive salp swarm algorithm(CASSA), is proposed in this paper. The Tent chaotic sequence is used to initiate the individuals’ position,which can strengthen the diversity of initiate the individuals. The crazy operator is introduced at the food source position to increase the diversity of the population. The adaptive inertial weight is introduced into the follower position update formula to balance the global search and local search ability of the algorithm. The efficiency of the CASSA is evaluated by using statistical analysis, convergence rate analysis, Wilcoxon’s test, standard deviations on classical benchmark functions and modern CEC 2014 functions. The results show that the CASSA has better global search ability and solving robustness,and meanwhile, the optimization accuracy and convergence speed are also more powerful than the standard algorithm.Especially, in solving the high-dimension and multimodal function optimization problem, the improved algorithm has better performance.