IEICE TRANSACTIONS on Fundamentals
An Adaptive Niching EDA with Balance Searching Based on Clustering Analysis
Summary :
For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.10 pp.1792-1799
- Publication Date
- 2010/10/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E93.A.1792
- Type of Manuscript
- PAPER
- Category
- VLSI Design Technology and CAD
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Benhui CHEN, Jinglu HU, "An Adaptive Niching EDA with Balance Searching Based on Clustering Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 10, pp. 1792-1799, October 2010, doi: 10.1587/transfun.E93.A.1792.
Abstract: For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.1792/_p
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@ARTICLE{e93-a_10_1792,
author={Benhui CHEN, Jinglu HU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Adaptive Niching EDA with Balance Searching Based on Clustering Analysis},
year={2010},
volume={E93-A},
number={10},
pages={1792-1799},
abstract={For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.},
keywords={},
doi={10.1587/transfun.E93.A.1792},
ISSN={1745-1337},
month={October},}
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TY - JOUR
TI - An Adaptive Niching EDA with Balance Searching Based on Clustering Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1792
EP - 1799
AU - Benhui CHEN
AU - Jinglu HU
PY - 2010
DO - 10.1587/transfun.E93.A.1792
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2010
AB - For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.
ER -
English
