School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian 350118, China.
National Demonstration Center for Experimental Electronic Information and Electrical Technology Education, Fujian University of Technology, Fuzhou, Fujian 350118, China.
Comput Intell Neurosci. 2022 May 27;2022:3418269. doi: 10.1155/2022/3418269. eCollection 2022.
To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.
为了解决鲸鱼优化算法（WOA）在全局搜索能力不足和收敛速度慢的缺点，本文提出了一种差分进化混沌鲸鱼优化算法（DECWOA）。首先，在算法开始时引入正弦混沌理论生成初始种群，以增加种群的多样性。其次，将新的自适应惯性权重引入个体鲸鱼位置更新公式中，为全局搜索奠定基础，提高算法的优化性能。最后，融合差分方差算法以提高鲸鱼优化算法的全局搜索速度和准确性。使用不同的测试函数随机选择，分析了各种改进策略对算法性能的影响。将粒子群优化算法（PSO）、蝴蝶优化算法（BOA）、WOA、混沌反馈自适应鲸鱼优化算法（CFAWOA）和 DECWOA 算法进行了比较，以获得最佳的搜索性能。使用 MATLAB 软件进行实验模拟，结果表明，改进后的鲸鱼优化算法具有更好的全局优化搜索能力。将改进的鲸鱼优化算法应用于 IEEE-33 节点的配电网故障定位中，验证了基于多策略改进鲸鱼优化算法的配电网故障区域定位的有效性和准确性。
