文献类型：期刊文献

英文题名：An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

作者：Xu, Dongxing;Yin, Jianchuan[1]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524088, Peoples R China; Guangdong Prov Engn Res Ctr Ship Intelligence & S, Zhanjiang 524088, Peoples R China

年份：2023

卷号：11

起止页码：32476

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000967362400001)、、EI(收录号：20231413850420)、Scopus(收录号：2-s2.0-85151532052)、WOS

基金：This work was supported in part by the National Natural Science Foundation of P. R. China under Grant 52271361 and Grant 52231014.

语种：英文

外文关键词：Optimization; Metaheuristics; Convergence; Heuristic algorithms; Search problems; Statistics; Markov processes; Black widow optimization algorithm; double chaotic map; golden sine algorithm (Gold-SA); Cauchy barycentric reverse difference mutation operator; Markov chain; engineering optimization

外文摘要：In solving engineering constrained optimization problems, the conventional black widow optimization algorithm (BWOA) has some shortcomings such as insufficient robustness and slow convergence speed. Therefore, an improved black widow optimization algorithm (IBWOA) is proposed by combining methods of double chaotic map, Cauchy center of gravity inverse difference mutation and golden sine guidance strategy. Firstly, the quality of the initial population of the BWOA is improved based on the double chaotic map; Secondly, in order to make full use of the difference information between the current and the optimal position thus improve optimization accuracy, the golden sine algorithm (Gold-SA) is introduced to update the position of the black widow individuals; Finally, the Cauchy barycenter reverse differential mutation operator is employed to increase the diversity of the population, avoid local optimization thus improve the global search ability of the algorithm. In addition, the global convergence characteristics of the IBWOA are analyzed based on the Markov process and the convergence probability reaches 1 for the globally optimal solution. The performance of the proposed IBWOA was evaluated based on eight continuous / discrete hybrid engineering optimization problems and typical benchmark functions. The results show that the improved BWOA can improve the search accuracy, convergence speed and robustness effectively comparing with some other conventional optimization algorithms.