Global optimization with the PSO coupling-type discrete gradient chaos model
Global optimization with the PSO coupling-type discrete gradient chaos model
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DOI：
10.1002/eej.20563
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发表时间：
2008-12-01
影响因子：
0.4
通讯作者：
Aiyoshi, Eitaro
中科院分区：
文献类型：
--
作者：
Okamoto, Takashi;Aiyoshi, Eitaro
本文利用粒子群算法所具有的耦合结构，提出了一种新的多智能体离散梯度混沌模型。具体而言，首先引入了一种多智能体优化模型，该模型采用最简单的动态全局搜索模型离散梯度混沌模型进行自主搜索，并采用对流耦合方式进行耦合。该模型采用对流耦合的方法，克服了原离散梯度混沌C模型存在的边界危机问题。其次，将种群向最优和最优漂移的PSO耦合结构引入离散梯度混沌模型；然后，我们提出了基于目标函数值的搜索策略的“粒子群耦合型离散梯度混沌模型”。在本文中，我们提出的模型应用于几个基准问题。结果表明，该模型比原有的离散梯度混沌模型和粒子群模型具有更好的全局优化能力。(C) 2008 Wiley期刊有限公司
In this paper, we propose a new multiagent discrete gradient chaos model using acoupling structure which PSO has. Concretely, first, we introduce a multiagent-type optimization model whose agents search autonomously with the discrete gradient chaos model which is the simplest dynamical global search model, and they are coupled by convective coupling. Convective Coupling in this model is used to aim at overcoming of emergence of boundary crisis which is a problem of the original discrete gradient chaos C model. Second, we introduce PSO Coupling Structure, where population drifts to the gbest and the pbest, into discrete gradient chaos model. Then, we propose "PSO coupling-type discrete gradient chaos model" with the search strategy based on objective function's value. In this paper, our proposed models are applied to several benchmark problems. The results show that our proposed models have better global optimization ability than the original discrete gradient chaos model and PSO model. (C) 2008 Wiley Periodicals, Inc.