| Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm. | |
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MedLine Citation:
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PMID: 21591888 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Abstract In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are de-emphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500-optima. Most past multimodal EA studies considered problems having a few variables. In this paper, we have solved up to 16-variable test-problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test-problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application. |
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Authors:
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Kalyanmoy Deb; Amit Saha |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-5-18 |
Journal Detail:
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Title: Evolutionary computation Volume: - ISSN: 1530-9304 ISO Abbreviation: - Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-5-19 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9513581 Medline TA: Evol Comput Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Kanpur Genetic Algorithms Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208 016, India. deb@iitk.ac.in. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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