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A New Hyper-Heuristic Multi-Objective Optimisation Approach Based on MOEA/D Framework.

Jiayi Han1, Shinya Watanabe1

  • 1Muroran institute of technology, Muroran 050-0000, Japan.

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Summary
This summary is machine-generated.

This study introduces a new hybrid algorithm for multi-objective optimization problems (MOPs) that dynamically switches between differential evolution (DE) and covariance matrix adaptation evolution strategy (CMA-ES) operators. The novel approach enhances efficiency and performance on complex optimization tasks.

Keywords:
CMA-ESIDEMOEA/Defficiency inspectionevolutionary multi-objective optimization (EMO)hyper-heuristic approachoperator switching mechanism

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Multi-objective evolutionary algorithms based on decomposition (MOEA/D) are effective for multi-objective optimization problems (MOPs).
  • Fixed offspring-generating strategies in MOEA/D can limit applicability, leading to interest in hybrid algorithms.
  • Understanding the advantages of hybrid approaches requires investigating dynamic strategy integration.

Purpose of the Study:

  • To propose a novel hyper-heuristic approach integrating estimation of distribution (ED) and crossover (CX) strategies into MOEA/D.
  • To dynamically switch between differential evolution (DE) and covariance matrix adaptation evolution strategy (CMA-ES) operators.
  • To investigate the role of success replacement rate (SRR) in explaining hybrid algorithm advantages.

Main Methods:

  • A hyper-heuristic framework is developed within MOEA/D.
  • Dynamic switching between DE and CMA-ES operators is implemented.
  • Improved Differential Evolution (IDE) is used for specific subproblems to manage evaluation costs.

Main Results:

  • The proposed approach demonstrates distinct advantages on a three-objective test suite.
  • Significant enhancement in the efficiency (Success Rate Ratio - SRR) of the DE operator is validated.
  • Experimental findings provide insights into the performance of hybrid evolutionary algorithms.

Conclusions:

  • The novel hyper-heuristic MOEA/D approach effectively integrates ED and CX strategies.
  • Dynamic operator switching enhances performance and efficiency in multi-objective optimization.
  • The study offers valuable perspectives on hybrid algorithms and SRR for future research.