DCWPSO:采用动态惯性权重更新和增强型学习策略的粒子群优化技术

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-09-03 DOI:10.7717/peerj-cs.2253
Yibo Han, Meiting Lin, Ni Li, Qi Qi, Jinqing Li, Qingxin Liu
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引用次数: 0

摘要

粒子群优化(PSO)是群集智能(SI)中一种杰出而稳健的元启发式算法。它起源于 1995 年模拟鸟群的觅食行为。近年来,针对各种优化应用提出了许多 PSO 变体。然而,这些变体的总体性能并不令人满意。本文介绍了一种新型 PSO 变体,并提出了三个主要贡献:首先,引入了一种新颖的动态振荡惯性权重,以在探索和开发之间取得平衡;其次,利用余弦相似性和动态邻域策略提高了解决方案的质量和粒子群的多样性;第三,提出了一种独特的最差实例学习策略,以提高最不利解决方案的质量,从而改善整体粒子群。该算法的验证使用了由 CEC2014 和 CEC2022 测试套件中的真实参数单目标优化基准组成的测试套件。实验结果表明,与最近提出的最先进 PSO 变体和著名算法相比,我们的算法具有很强的竞争力。
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DCWPSO: particle swarm optimization with dynamic inertia weight updating and enhanced learning strategies
Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within swarm intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various optimization applications. However, the overall performance of these variants has not been deemed satisfactory. This article introduces a novel PSO variant, presenting three key contributions: First, a novel dynamic oscillation inertia weight is introduced to strike a balance between exploration and exploitation; Second, the utilization of cosine similarity and dynamic neighborhood strategy enhances both the quality of solution and the diversity of particle populations; Third, a unique worst-best example learning strategy is proposed to enhance the quality of the least favorable solution and consequently improving the overall population. The algorithm’s validation is conducted using a test suite comprised of benchmarks from the CEC2014 and CEC2022 test suites on real-parameter single-objective optimization. The experimental results demonstrate the competitiveness of our algorithm against recently proposed state-of-the-art PSO variants and well-known algorithms.
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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