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....
| Publicado en: | Scientific World Journal pp. 836272 - 836273 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103842936&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103842936 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103842936 103842936 NLM25170526 2012704895 10.1155/2014/836272 NLM25170526 PMC4134823 103842936 ppf: 836272 ppct: 1 formats: tig: atl: Multiobjective memetic estimation of distribution algorithm based on an incremental tournament local searcher. aug: au: Yang, Kaifeng Mu, Li Yang, Dongdong Zou, Feng Wang, Lei Jiang, Qiaoyong affil: School of Computer Science and Engineering, Xi'an University of Technology, P.O. Box 666, No. 5 South Jinhua Road, Xi'an 710048, China. sug: subj: Algorithms ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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