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Multi-objective grasshopper optimization algorithm based on multi-group and co-evolution.

Chao Wang1,2, Jian Li1,2, Haidi Rao1,2

  • 1Anhui Agricultural University, Hefei 230036, China.

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|April 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-group and Co-evolution Framework to balance exploration and exploitation in meta-heuristic optimization, significantly improving convergence and diversity for multi-objective problems.

Keywords:
grasshopper optimization algorithmmeta-heuristicsmulti-objective optimizationswam intelligence algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Balancing exploration and exploitation is crucial for meta-heuristic optimization performance.
  • Existing methods struggle to maintain an optimal tradeoff throughout the search process.

Purpose of the Study:

  • To develop a Multi-group and Co-evolution Framework for the Multi-objective Grasshopper Optimization Algorithm (MOGOA).
  • To enhance the balance between exploration and exploitation in meta-heuristic optimization.

Main Methods:

  • Designed a grouping mechanism to improve search agent diversity and search space coverage.
  • Integrated a co-evolution mechanism to enhance convergence to the Pareto optimal front through agent interaction.
  • Benchmarked the enhanced MOGOA against standard test functions (CEC2009, ZDT, DTLZ).

Main Results:

  • The framework significantly improved MOGOA's convergence accuracy and speed.
  • Quantitative and qualitative analyses showed substantial improvements in the diversity and convergence of multi-objective solutions.
  • Performance indicators (GD and IGD) demonstrated significant enhancements, with some metrics more than doubling.

Conclusions:

  • The Multi-group and Co-evolution Framework effectively balances exploration and exploitation.
  • The proposed approach demonstrably enhances the performance of multi-objective optimization algorithms.
  • Results were validated using statistical tests, confirming the significant improvements achieved.