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The review of multiple evolutionary searches and multi-objective evolutionary algorithms

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Abstract

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.

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  1. Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 , Skudai, Johor Bahru, Malaysia

    Hossein Rajabalipour Cheshmehgaz & Habibollah Haron

  2. Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Abdollah Sharifi

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  1. Hossein Rajabalipour Cheshmehgaz
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  2. Habibollah Haron
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Correspondence to Hossein Rajabalipour Cheshmehgaz.

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In Memory of a beautiful girl, Siran Yeganeh who has sadly died from her injuries in an elementary school fire accident in the northwestern Iranian village of Sheenabad - by Hossein Rajabalipour C.

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Cheshmehgaz, H.R., Haron, H. & Sharifi, A. The review of multiple evolutionary searches and multi-objective evolutionary algorithms. Artif Intell Rev 43, 311–343 (2015). https://doi.org/10.1007/s10462-012-9378-3

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