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Adaptively weighted decomposition based multi-objective evolutionary algorithm

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Abstract

Multi-objective evolutionary algorithm based on Decomposition (MOEA/D) decomposes a multi-objective problem into a number of scalar optimization problems using uniformly distributed weight vectors. However, uniformly distributed weight vectors do not guarantee uniformity of solutions on approximated Pareto-Front. This study proposes an adaptive strategy to modify these scalarizing weights after regular intervals by assessing the crowdedness of solutions using crowding distance operator. Experiments carried out over several benchmark problems with complex Pareto-Fronts show that such a strategy helps in improving the convergence and diversity of solutions on approximated Pareto-Front. Proposed algorithm also shows better performance when compared with other state-of-the-art multi-objective algorithms over most of the benchmark problems.

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Acknowledgements

The authors wish to thank anonymous reviewers and the editor-in-chief for their insightful comments and timely feedback.

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The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Suraj S. Meghwani

  2. School of Basic Sciences, Indian Institute of Technology, Mandi, Himachal Pradesh, India

    Manoj Thakur

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  1. Suraj S. Meghwani
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  2. Manoj Thakur
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Meghwani, S.S., Thakur, M. Adaptively weighted decomposition based multi-objective evolutionary algorithm. Appl Intell 51, 3801–3823 (2021). https://doi.org/10.1007/s10489-020-01969-w

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