数学优化
算法
局部最优
计算机科学
趋同(经济学)
水准点(测量)
差异进化
早熟收敛
人口
最优化问题
数学
粒子群优化
社会学
人口学
经济
经济增长
地理
大地测量学
作者
Chenxin Wan,Bitao He,Yuancheng Fan,Wei Tan,Tao Qin,Jing Yang
出处
期刊:Entropy
[Multidisciplinary Digital Publishing Institute]
日期:2022-11-11
卷期号:24 (11): 1640-1640
被引量:4
摘要
The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (IBWOA). First, Gauss chaotic mapping is introduced to initialize the population to ensure the diversity of the algorithm at the initial stage. Then, the sine cosine strategy is introduced to perturb the individuals during iteration to improve the global search ability of the algorithm. In addition, the elite opposition-based learning strategy is introduced to improve convergence speed of algorithm. Finally, the mutation method of the differential evolution algorithm is integrated to reorganize the individuals with poor fitness values. Through the analysis of the optimization results of 13 benchmark test functions and a part of CEC2017 test functions, the effectiveness and rationality of each improved strategy are verified. Moreover, it shows that the proposed algorithm has significant improvement in solution accuracy, performance and convergence speed compared with other algorithms. Furthermore, the IBWOA algorithm is used to solve six practical constrained engineering problems. The results show that the IBWOA has excellent optimization ability and scalability.
科研通智能强力驱动
Strongly Powered by AbleSci AI