基于多维度变异学习与收散归优的鲸鱼优化算法

Whale optimization algorithm based on multi-dimensional variation learning and distributed optimization

针对鲸鱼优化算法(WOA)在解决高维、多峰、最优值非原点等问题时存在的收敛精度低、易被局部最优捕获等缺陷,提出了一种基于多维度变异学习与收散归优的鲸鱼优化算法(MLDOWOA)。首先,引入自适应权值以及优势个体干扰动态调整个体螺旋包围的方向,提高了算法的全局搜索能力和收敛精度;然后提出多维度变异学习机制对种群变异方向进行自适应规划,进一步扩大了算法的搜索范围;最后引入收散归优机制协调了搜索步长,帮助种群突破了中后期搜索停滞的局限。通过8个高维基准函数和4个固定维基准函数对MLDOWOA算法进行测试,结果表明同基本算法WOA、SSA以及改进的ACWOA、AWOA、MSIWOA、ADWOA相比,该算法在收敛精度和应对高维函数的能力上具有显著的优越性。将该算法应用于FOPID控制器的参数整定,并将实验结果同近年来该工程问题的研究成果进行对比分析,证明了该算法在FOPID参数整定问题中具有卓越的性能。

Aiming at the shortcomings of low convergence accuracy and easy to be captured by local optimum when whale optimization algorithm(WOA)solved problems such as high dimension,multi-peak and non-origin of optimal value,this paper proposed a whale optimization algorithm based on multi-dimensional variation learning and distributed optimization(MLDOWOA).Firstly,this algorithm introduced adaptive weights and dominant individual interference to dynamically adjust the direction of individual spiral surround,which improved the global search ability and convergence accuracy of the algorithm.Then,in order to further expand the search range of the algorithm,it used a multi-dimensional variation learning mechanism to adaptively plan the direction of population variation.Finally,it put forward the distributed optimization mechanism to coordinate the step size of search,so as to help the population break through the limitation of search stagnation in the middle and late stage.The test results on 8 high dimensional benchmark functions and 4 fixed dimensional benchmark functions show that,compared with the basic WOA,SSA and the improved ACWOA,AWOA,MSIWOA,ADWOA,the MLDOWOA has significant advantages in convergence accuracy and ability to cope with high dimensional functions.This paper used the MLDOWOA to tune the parameters of the FOPID controller,and compared the control results with the research results of this engineering problem in recent years.It proves the excellent performance of the proposed algorithm in FOPID parameter tuning problems.