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Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed....

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Detalles Bibliográficos
Autores principales: Yang, Kaifeng, Mu, Li, Yang, Dongdong, Zou, Feng, Wang, Lei, Jiang, Qiaoyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134823/
https://www.ncbi.nlm.nih.gov/pubmed/25170526
http://dx.doi.org/10.1155/2014/836272
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author Yang, Kaifeng
Mu, Li
Yang, Dongdong
Zou, Feng
Wang, Lei
Jiang, Qiaoyong
author_facet Yang, Kaifeng
Mu, Li
Yang, Dongdong
Zou, Feng
Wang, Lei
Jiang, Qiaoyong
author_sort Yang, Kaifeng
collection PubMed
description A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.
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spelling pubmed-41348232014-08-28 Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher Yang, Kaifeng Mu, Li Yang, Dongdong Zou, Feng Wang, Lei Jiang, Qiaoyong ScientificWorldJournal Research Article A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics. Hindawi Publishing Corporation 2014 2014-07-23 /pmc/articles/PMC4134823/ /pubmed/25170526 http://dx.doi.org/10.1155/2014/836272 Text en Copyright © 2014 Kaifeng Yang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yang, Kaifeng
Mu, Li
Yang, Dongdong
Zou, Feng
Wang, Lei
Jiang, Qiaoyong
Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher
title Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher
title_full Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher
title_fullStr Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher
title_full_unstemmed Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher
title_short Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher
title_sort multiobjective memetic estimation of distribution algorithm based on an incremental tournament local searcher
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134823/
https://www.ncbi.nlm.nih.gov/pubmed/25170526
http://dx.doi.org/10.1155/2014/836272
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