【摘要】 针对原始鲸鱼优化算法(WOA)收敛速度慢、全局搜索能力弱、求解精度低且易陷入局部最优等问题，提出一种混合策略来改进的鲸鱼优化算法(LGWOA)。首先将莱维飞行引入鲸鱼全局搜索的公式中，通过莱维飞行加大全局搜索步长，扩大搜索空间、提高全局搜索能力;其次，在鲸鱼螺旋上升阶段，加入一个自适应权重参数来提高算法的局部搜索能力和求解精度;最后结合遗传算法的交叉变异思想平衡算法的全局搜索和局部搜索能力，维持种群的多样性，规避陷入局部最优。通过对12个基准测试函数从2个角度进行实验对比分析，结果表明，基于混合策略改进的鲸鱼优化算法在收敛速度和求解精度上均有明显提升。更多还原

【Abstract】 In order to solve the problems of the original whale optimization algorithm( WOA) with slow convergence speed,weak global search ability,low solution accuracy and easy to fall into local optimization,a hybrid strategy is proposed to improve the whale optimization algorithm( LGWOA). Firstly,the Levy flight strategy is introduced into the position update formula of the whale random search,and the global search step is increased through Levy flight,the search space is enlarged,and the global search capability is improved. Secondly,the adaptive weight is introduced into the whale spiral upward position update formula to improve the algorithm’s local search ability and optimization accuracy. Finally,the idea combining the genetic algorithm’s cross mutation is used to balance the algorithm’s global search and local search capabilities,maintain the diversity of the population,and avoid falling into the local optimum. Simulation experiments on 12 benchmark test functions in different dimensions show that the improved whale algorithm has faster convergence speed and higher optimization accuracy.更多还原