一种基于交叉选择的柯西反向鲸鱼优化算法

Quasi-Oppositional Whale Optimization Algorithm Based on Crossover and Selection Strategy

针对传统鲸鱼优化算法求解精度不高、容易陷入局部最优的缺点,提出了一种基于交叉选择策略的柯西反向鲸鱼优化算法。在鲸鱼优化算法中引入柯西反向学习技术以加快算法的收敛速度;对鲸鱼优化算法中的种群个体进行交叉和选择操作以提高算法的求解精度。对引入不同改进策略的鲸鱼优化算法在Matlab软件中进行仿真测试,结果表明:与基本鲸鱼优化算法相比,所提算法的收敛速度和寻优精度有显著提升,在大规模传感器优化管理方面具有十分重要的工程应用价值。

The whale optimization algorithm(WOA)has low convergence accuracy and it is easily trapped into local optima.A quasi-oppositional whale optimization algorithm based on crossover and selection strategy(QOWOA-CS)was proposed to overcome these shortcomings of WOA.The quasi-oppositional learning was incorporated into WOA to accelerate the convergence speed.The search agents of WOA updated their positions by using crossover and selection strategy to improve the solution accuracy.The WOA algorithms with different improvement strategies were simulated in the Matlab software.The experiment results show that the convergence speed and solution accuracy of proposed algorithm are improved obviously,and this proposed algorithm has great application value for large-scale sensor management problem.