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Multi-objective evolutionary multitasking algorithm based on cross-task transfer solution matching strategy.

Hao Sun1, Pengfei Chen1, Ziyu Hu1

  • 1Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China.

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|March 22, 2023
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Summary
This summary is machine-generated.

This study introduces a bidirectional knowledge transfer method for evolutionary multitasking (EMT) algorithms. By considering target task preferences, it enhances synergy and improves optimization performance compared to existing unidirectional approaches.

Keywords:
Evolutionary algorithmKnowledge transferMulti-objective optimizationMultitasking optimization

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Area of Science:

  • Artificial Intelligence
  • Computational Optimization
  • Evolutionary Computation

Background:

  • Evolutionary multitasking (EMT) algorithms leverage task synergy for superior performance.
  • Current EMT methods use unidirectional knowledge transfer, limiting full synergy utilization.
  • Unidirectional transfer ignores target task search preferences, hindering optimization.

Purpose of the Study:

  • To propose a bidirectional knowledge transfer method for EMT algorithms.
  • To enhance the utilization of potential synergy between tasks.
  • To introduce an adaptive strategy for dynamic knowledge transfer intensity adjustment.

Main Methods:

  • Developed a bidirectional knowledge transfer mechanism considering target task search preferences.
  • Implemented an adaptive strategy to regulate knowledge transfer intensity based on individual conditions.
  • Evaluated the proposed algorithm against existing methods on 38 multi-objective multitasking optimization benchmarks.

Main Results:

  • The proposed bidirectional EMT algorithm significantly outperformed comparison algorithms on over 30 benchmarks.
  • Demonstrated considerable convergence efficiency alongside improved performance.
  • The adaptive strategy effectively balanced population convergence and computational load.

Conclusions:

  • Bidirectional knowledge transfer in EMT effectively utilizes task synergy.
  • The adaptive strategy provides dynamic control over knowledge transfer intensity.
  • The proposed method offers a more efficient and effective approach to multi-objective optimization problems.