用于解决 NP 难优化问题的自适应多策略粒子群优化法

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Houda Abadlia, Imhamed R. Belhassen, Nadia Smairi
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引用次数: 0

摘要

粒子群优化算法(PSO)因其实施简单、处理各种测试功能和工程优化问题的效率高而被广泛用于解决优化问题。然而,PSO 会遇到过早收敛和缺乏多样性等问题,尤其是在面对复杂的高维优化任务时。在本研究中,我们提出了岛屿模型粒子群优化(IMPSO)的增强版,根据几种迁移策略将岛屿模型集成到 PSO 算法中。第一个贡献在于应用了基于塔布搜索技术的新选择和替换策略,第二个贡献在于提出了基于生物地理学优化技术的动态迁移率。为了评估和验证所提方法的有效性,应用了几个无约束基准函数。结果证实,在解决 NP 难优化问题时,该方法比旧版 IMPSO 性能更好。与其他著名的进化算法相比,所提出的方法更加高效和有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive multi-strategy particle swarm optimization for solving NP-hard optimization problems
Particle Swarm Optimization algorithm (PSO) has been widely utilized for addressing optimization problems due to its straightforward implementation and efficiency in tackling various test functions and engineering optimization problems. Nevertheless, PSO encounters issues like premature convergence and a lack of diversity, particularly when confronted with complex high-dimensional optimization tasks. In this study, we propose an enhanced version of the Island Model Particle Swarm Optimization (IMPSO), where island models are integrated into the PSO algorithm based on several migration strategies. The first contribution consists in applying a new selection and replacement strategies based on tabu search technique, while the second contribution consists in proposing a dynamic migration rate relying on the Biogeography-Based Optimization technique. To assess and validate the effectiveness of the proposed method, several unconstrained benchmark functions are applied. The obtained results confirm that the approach yield better performance than the old version of IMPSO for solving NP-hard optimization problems. Compared to the performance of other well-known evolutionary algorithms, the proposed approach is more efficient and effective.
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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
22
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