中心多群协同粒子群优化：比率与比例学习策略

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本文主要探讨了一种中心多群协作粒子 swarm 运算优化（ACenterMulti-swarm Cooperative Particle Swarm Optimization with Ratio and Proportion Learning, CMCPSO-RP）。该方法源于两个著名的心理学理论，旨在解决原始多群协作粒子 swarm (MCPSO) 中存在的问题，特别是收敛速度过快导致的多样性降低和早熟收敛现象。在原始的 MCPSO 中，为了加速收敛，可能会牺牲子群之间的多样性，这可能导致搜索空间的局部最优区域未得到有效探索。为解决这一问题，CMCPSO-RP 引入了一个创新机制：所有子群中的最佳粒子被收集并传递给主群，形成潜在解的群体。这种方法减少了陷入局部极小值的风险，但迭代效率相对较低。为平衡探索与利用的能力，作者借鉴认知负荷理论，提出了一个比率和比例学习策略。这个策略赋予搜索粒子类似人类思维和认知过程的能力，通过动态调整每个粒子在不同阶段对全局最优解的追求程度和对周围区域的探索范围。具体来说，比率学习控制了粒子对当前最佳位置的依赖，而比例学习则决定粒子在搜索过程中的分散程度，以保持算法在不断探索新解的同时，避免过于集中的搜索导致的性能瓶颈。通过结合这些新颖的策略，CMCPSO-RP 在保持搜索多样性的前提下，提高了搜索效率和全局最优解的发现概率。它在优化问题求解中展现出了良好的适应性和鲁棒性，特别是在处理复杂且多模态优化问题时，能够有效地防止过早收敛，并提供更全面的解决方案空间覆盖。研究者们期望这种中心多群协作与智能学习相结合的方法能够在众多实际应用中展现出优越的性能，如机器学习、工程设计优化等领域。

A Center Multi-swarm Cooperative Particle

Swarm Optimization with Ratio

and Proportion Learning

Xuemin Liu, Lili

(&)

, and Jiaoju Ge

(&)

Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China

ximlli@126.com, jiaoge@hit.edu.cn

Abstract. This paper presents a center multi-swarm cooperative PSO with ratio

and proportion learning (CMCPSO-RP), employing two well-known psychol-

ogy theories. In the original MCPSO-CC, the convergence speed can be

accelerated which comes at decreasing the diversity of sub-swarms, suffering

from premature convergence. There is no mechanism to guarantee every pos-

sible region of the search space could be searched. To tackle this problem, all

best particles from each sub-swarm can be collected and sent to master swarm to

maintain a population of potential solutions. This process is less prone to

becoming trapped in local minima, but typically has lower efﬁciency of itera-

tions. To balance the ability of exploration and exploitation, a ratio and pro-

portion learning strategy is proposed by empowering the searching particles

with human-like thinking and cognitive process, inspired by Cognitive Load

Theory and Human Problem Solving Theory. In our approach, a reasonable ratio

design can be not only a way to exhibit a solution quality versus speed tradeoff,

but also make CMCPSO-RP more in line with the laws of regular learning in

nature. Application of the newly developed PSO algorithm on several bench-

mark optimization problems shows a marked improvement in performance over

the comparison algorithms on all test functions.

Keywords: Multi-swarm

 Center communication  Ratio and proportion

learning

1 Introduction

Particle swarm optimization (PSO) was ﬁrst developed by Kennedy and Eberhart in

1995 [1]. For general problems, it provides efﬁcient and satisfactory solutions like other

meta-heuristic methods and search algorithms [2], such as genetic algorithms (GA) [3]

and differential evolution (DE) [4]. Owing to its simple concept and high efﬁciency,

PSO has been used to solve a range of optimization problems [5], including neural

network training [6], data mining [7], feature selection [8], just to name a few. Con-

sidering the fact that the original PSO works better in simple and low-dimensional

search space, various attempts have been made to improve its performance from

aspects of topology, parameter control mechanism, learning strategy and hybridization.

For solving complex multimodal problems, numerous multi-swarm techniques, aiming

to improve the population diversity, are studied, such as dynamic multi-swarm PSO

Y. Tan et al. (Eds.): ICSI 2017, Part I, LNCS 10385, pp. 189–197, 2017.

DOI: 10.1007/978-3-319-61824-1_21

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