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A memetic optimization strategy based on dimension reduction in decision space.

Handing Wang1, Licheng Jiao, Ronghua Shang

  • 1Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center of Intelligent Perception and Computation, Xidian University, Xi'an, 710071, China wanghanding@163.com.

Evolutionary Computation
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Summary
This summary is machine-generated.

This study introduces a dimension reduction strategy for multi-objective optimization problems (MOPs) with unbalanced variable-objective mappings. The novel approach enhances convergence and diversity by optimizing in reduced subspaces.

Keywords:
Multi-objective optimizationdimension reductionevolutionary algorithmlocal searchmemetic algorithmportability

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Area of Science:

  • Optimization
  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Multi-objective optimization problems (MOPs) often exhibit complex, unbalanced relationships between decision variables and objective functions.
  • This imbalance can lead to inefficiencies and redundancy in the search process within the decision space.

Purpose of the Study:

  • To propose a novel memetic optimization strategy, dimension reduction in decision space (DRMOS), to address unbalanced variable-objective mappings in MOPs.
  • To enhance the efficiency and effectiveness of multi-objective evolutionary algorithms (MOEAs) when dealing with complex MOPs.

Main Methods:

  • DRMOS analyzes the mapping relationship between decision variables and objective functions.
  • It reduces the search space dimension by partitioning the decision space into subspaces based on this analysis.
  • Memetic local search strategies are applied independently within these subspaces to improve the population.

Main Results:

  • DRMOS demonstrated advantages in convergence speed and diversity maintenance for MOPs with unbalanced mappings.
  • The strategy showed good portability and compatibility with existing state-of-the-art MOEAs.
  • Experiments confirmed DRMOS's effectiveness when embedded in various MOEAs.

Conclusions:

  • DRMOS offers a robust method for tackling MOPs characterized by unbalanced variable-objective relationships.
  • The strategy's dimension reduction and subspace optimization approach improves performance metrics.
  • Its compatibility with existing MOEAs highlights its practical applicability and potential for broader adoption.