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Hybrid selection based multi/many-objective evolutionary algorithm.

Saykat Dutta1, Rammohan Mallipeddi2, Kedar Nath Das1

  • 1Department of Mathematics, National Institute of Technology Silchar, Silchar, India.

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|April 28, 2022
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Summary
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A new Hybrid Selection based Multi-Objective Evolutionary Algorithm (HS-MOEA) effectively balances diversity and convergence. HS-MOEA outperforms existing algorithms on complex multi-objective problems.

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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Multi-objective evolutionary algorithms (MOEAs) are crucial for tackling complex multi-objective problems (MOPs).
  • Existing MOEAs face challenges with discontinuous or degenerate Pareto Fronts (PFs).
  • Current MOEAs are categorized into dominance-based, decomposition-based, and indicator-based approaches, each with limitations.

Purpose of the Study:

  • To introduce a novel Hybrid Selection based MOEA (HS-MOEA).
  • To address limitations in existing MOEAs for diverse MOP characteristics.
  • To improve the balance between convergence and diversity in evolutionary optimization.

Main Methods:

  • Developed a new environmental selection strategy for MOEAs.
  • Integrated Pareto-dominance, reference vectors, and indicator-based concepts.
  • Utilized DTLZ and WFG test suites for comprehensive performance evaluation.

Main Results:

  • HS-MOEA demonstrates a superior ability to balance diversity and convergence.
  • Experimental simulations confirm the effectiveness of the proposed hybrid selection strategy.
  • The algorithm shows improved performance compared to state-of-the-art MOEAs on challenging MOPs.

Conclusions:

  • HS-MOEA offers a simple yet effective hybridization of existing MOEA concepts.
  • The proposed method enhances performance on MOPs with up to 10 objectives.
  • HS-MOEA represents a significant advancement in multi-objective evolutionary optimization.