影响因子：8.5
DOI码：10.1016/j.swevo.2024.101811
所属单位：College of Shipbuilding Engineering, Harbin Engineering University
发表刊物：Swarm and Evolutionary Computation
刊物所在地：荷兰
项目来源：Key Research and Development Program of Hainan Province (ZDYF2023GXJS017); National Natural Science
关键字：Aroa; Chaoitc initialization; Crossover operation; Quantum mutation
摘要：The Attraction-Repulsion Optimization Algorithm (AROA) is a novel optimization algorithm that balances exploration and exploitation by mimicking the natural equilibrium associated with attraction-repulsion phenomena. While AROA exhibits strong optimization capabilities and fast convergence speed, its global search ability is relatively weak, making it prone to local optima in later iterations. To address these issues, this paper proposes a chaotic crossover quantum attraction-repulsion optimization algorithm (CCQAROA). First, a two-dimensional Hénon-Sine hyperchaotic map is introduced for population initialization to achieve a more uniformly distributed initial population, thereby improving convergence speed. Second, a crossover strategy inspired by differential evolution is applied, probabilistically retaining dimensional information from parent individuals to balance exploration and exploitation. Finally, a novel quantum mutation strategy is introduced, which perturbs the global best solution when the algorithm stagnates, helping the algorithm escape from local optima. The CCQAROA was tested using the CEC2014, CEC2017, and CEC2022 benchmark test suites. The performance of CCQAROA was compared with nine advanced algorithms, and the results demonstrated that CCQAROA can attain competitive or even better results. Furthermore, CCQAROA was applied to solve three engineering problems, and the results confirmed that the proposed improvements to AROA are both feasible and effective.
备注：Key Research and Development Program of Hainan Province (ZDYF2023GXJS017); National Natural Science Foundation of China (No. 52371315); Natural Science Foundation of Heilongjiang Province of China (ZD2022E002); Natural Science Foundation of Heilongjiang Province of China (GY2023ZB0033); and Key Research and Development Program of Heilongjiang Province (GA23A905).
合写作者：李向阳,王宇田,杨忠仪
第一作者：李明伟
论文类型：期刊论文
通讯作者：洪维强
论文编号：101811
学科门类：工学
文献类型：J
卷号：92
ISSN号：2210-6502
是否译文：否
发表时间：2025
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2210650224003493?via%3Dihub