基于COBL和FDB的改进鲸鱼优化算法
Improved Whale Optimization Algorithm Based on COBL and FDB
郭子瑜 1乔栋 2朱守健 1魏荣新1
作者信息
- 1. 山西大同大学煤炭工程学院,山西 大同 037009
- 2. 山西大同大学建筑与测绘工程学院,山西 大同 037009
- 折叠
摘要
针对鲸鱼优化算法(WOA)对初始解的依赖较强、容易陷入局部最优等问题,提出了一种基于复合策略反对立学习和适应度—距离平衡策略的改进的鲸鱼优化算法(CFWOA).首先在种群初始化阶段采用复合策略反对立学习来随机生成初始解.然后采用基于适应度—距离平衡的策略来更新迭代过程中产生新个体的位置.最后,通过6个基准测试函数,将CFWOA和原始鲸鱼优化算法(WOA)、遗传算法(GA)和粒子群算法(PSO)进行仿真实验对比.结果表明,CFWOA相比其他三种算法具有更高的搜索效率和全局搜索能力.
Abstract
To solve the problems of Whale Optimization Algorithm(WOA),such as strong dependence on initial solution and easy to fall into local optimality,COBL and FDB Whale Optimization Algorithm(CFWOA)is proposed.Firstly,in the population initial-ization stage,a compound strategy is used to randomly generate the initial solution.Secondly,the fitness-distance balance strategy is used to update the location of new individuals in the iterative process.Finally,CFWOA is compared with the original WOA,Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)through 6 benchmark test functions.The results show that CFWOA has higher search efficiency and global search ability than the other three algorithms.
关键词
鲸鱼优化算法/复合反对立学习/适应度-距离平衡/基准测试函数Key words
Whale Optimization Algorithm/compound opposition learning/fitness-distance balance/benchmark function引用本文复制引用
出版年
2024
NETL
NSTL
万方数据