TY - JOUR
T1 - The global optimization with the PSO coupling type discrete gradient chaos model
AU - Okamoto, Takashi
AU - Aiyoshi, Eitaro
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - In this paper, we propose a new multi-agent discrete gradient chaos model using a coupling structure which PSO has. Concretely, firstly, we introduce a multi-agent 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 a overcoming of emergence of boundary crisis which is a problem of original discrete gradient chaos model. Secondary, 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 proposal models are applied to several benchmark problems. The results show that our proposed models have better global optimization ability than original discrete gradient chaos model and PSO model.
AB - In this paper, we propose a new multi-agent discrete gradient chaos model using a coupling structure which PSO has. Concretely, firstly, we introduce a multi-agent 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 a overcoming of emergence of boundary crisis which is a problem of original discrete gradient chaos model. Secondary, 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 proposal models are applied to several benchmark problems. The results show that our proposed models have better global optimization ability than original discrete gradient chaos model and PSO model.
KW - Chaos
KW - Coupling model
KW - Global optimization
KW - Gradient system
KW - Multi-agent system model
KW - Particle swarm optimization
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U2 - 10.1541/ieejeiss.126.857
DO - 10.1541/ieejeiss.126.857
M3 - Article
AN - SCOPUS:33745786393
SN - 0385-4221
VL - 126
SP - 857
EP - 864
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
IS - 7
ER -