The review of multiple evolutionary searches and multi-objective evolutionary algorithms
Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has been identified as an effective method to search for optimal solutions of multi-objective optimization problems (MOPs). The existing multi-objective evolutionary algorithms that benefit from the multi...
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2015
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my.utm.589882021-12-14T08:52:53Z http://eprints.utm.my/id/eprint/58988/ The review of multiple evolutionary searches and multi-objective evolutionary algorithms Cheshmehgaz, Hossein Rajabalipour Haron, Habibollah Sharifi, Abdollah QA75 Electronic computers. Computer science Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has been identified as an effective method to search for optimal solutions of multi-objective optimization problems (MOPs). The existing multi-objective evolutionary algorithms that benefit from the multiple local searches (multiple-MOEAs, or MMOEAs) use different dividing methods and/or collaborations (information sharing) strategies between the created divisions. Their local evolutionary searches are implicitly or explicitly guided toward a part of global optimal solutions instead of converging to local ones in some divisions. In this reviewed paper, the dividing methods and the collaborations strategies are reviewed, while their advantage and disadvantage are mentioned. Kluwer Academic Publishers 2015 Article PeerReviewed Cheshmehgaz, Hossein Rajabalipour and Haron, Habibollah and Sharifi, Abdollah (2015) The review of multiple evolutionary searches and multi-objective evolutionary algorithms. Artificial Intelligence Review, 43 (3). pp. 311-343. ISSN 0269-2821 http://dx.doi.org/10.1007/s10462-012-9378-3 DOI:10.1007/s10462-012-9378-3 |
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QA75 Electronic computers. Computer science Cheshmehgaz, Hossein Rajabalipour Haron, Habibollah Sharifi, Abdollah The review of multiple evolutionary searches and multi-objective evolutionary algorithms |
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Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has been identified as an effective method to search for optimal solutions of multi-objective optimization problems (MOPs). The existing multi-objective evolutionary algorithms that benefit from the multiple local searches (multiple-MOEAs, or MMOEAs) use different dividing methods and/or collaborations (information sharing) strategies between the created divisions. Their local evolutionary searches are implicitly or explicitly guided toward a part of global optimal solutions instead of converging to local ones in some divisions. In this reviewed paper, the dividing methods and the collaborations strategies are reviewed, while their advantage and disadvantage are mentioned. |
format |
Article |
author |
Cheshmehgaz, Hossein Rajabalipour Haron, Habibollah Sharifi, Abdollah |
author_facet |
Cheshmehgaz, Hossein Rajabalipour Haron, Habibollah Sharifi, Abdollah |
author_sort |
Cheshmehgaz, Hossein Rajabalipour |
title |
The review of multiple evolutionary searches and multi-objective evolutionary algorithms |
title_short |
The review of multiple evolutionary searches and multi-objective evolutionary algorithms |
title_full |
The review of multiple evolutionary searches and multi-objective evolutionary algorithms |
title_fullStr |
The review of multiple evolutionary searches and multi-objective evolutionary algorithms |
title_full_unstemmed |
The review of multiple evolutionary searches and multi-objective evolutionary algorithms |
title_sort |
review of multiple evolutionary searches and multi-objective evolutionary algorithms |
publisher |
Kluwer Academic Publishers |
publishDate |
2015 |
url |
http://eprints.utm.my/id/eprint/58988/ http://dx.doi.org/10.1007/s10462-012-9378-3 |
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13.18916 |