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A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

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  • Guo-Zhong Fu
  • Tianda Yu
  • Wei Li
  • Qiang Deng
  • Bo Yang

Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is the seminal framework of multiobjective evolutionary algorithms (MOEAs). To alleviate the nonuniformly distributed solutions generated by a fixed set of evenly distributed weight vectors in the presence of nonconvex and disconnected problems, an adaptive vector generation mechanism is proposed. A coevolution strategy and a vector generator are synergistically cooperated to remedy the weight vectors. Optimal weight vectors are generated to replace the useless weight vectors to assure that optimal solutions are distributed evenly. Experiment results indicate that this mechanism is efficient in improving the diversity of MOEA/D.

Suggested Citation

  • Guo-Zhong Fu & Tianda Yu & Wei Li & Qiang Deng & Bo Yang, 2021. "A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, September.
    • Handle: RePEc:hin:jnlmpe:2764558
    DOI: 10.1155/2021/2764558
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    Cited by:

    1. Weiwei Wang & Biao Zhang & Baoxian Jia, 2023. "A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots," Sustainability, MDPI, vol. 15(3), pages 1-29, February.

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