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A multi-objective multi-tasking evolutionary algorithm based inverse mapping and adaptive transformation strategy:

Qinnan Wei1, Jingming Yang1, Ziyu Hu1

  • 1School of Electrical and Engineering, Yanshan University, Qinhuangdao, 066004, China.

ISA Transactions
|October 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved multi-objective multi-tasking optimization algorithm (IM-MFEA) to mitigate negative knowledge transfer. The novel approach enhances optimization performance by intelligently transferring knowledge between related tasks.

Keywords:
Evolutionary algorithmInverse modelMulti-objective optimizationMulti-tasking optimization

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Area of Science:

  • Computational Intelligence
  • Multi-task Optimization
  • Machine Learning

Background:

  • Multi-tasking optimization algorithms leverage knowledge transfer for simultaneous processing of related tasks.
  • Negative knowledge transfer can degrade algorithm performance.
  • Existing methods struggle to effectively mitigate negative knowledge transfer impacts.

Purpose of the Study:

  • To propose an improved multi-objective multi-tasking optimization algorithm (IM-MFEA) that reduces the negative impact of knowledge transfer.
  • To enhance the accuracy of knowledge transfer through inverse model mapping and objective transformation.
  • To improve the overall performance of multi-task optimization algorithms.

Main Methods:

  • Developed an inverse model mapping strategy incorporating correlation analysis to improve inverse mapping accuracy.
  • Implemented an adaptive objective transformation strategy to enhance source domain solution quality.
  • Reconstructed transformed source domain solutions via the inverse mapping strategy.
  • Integrated reconstructed source domain solutions with target domain solutions to generate superior offspring individuals.

Main Results:

  • Comprehensive experiments were conducted on nine multi-objective multi-factorial optimization (MFO) benchmark problems.
  • IM-MFEA demonstrated superior performance compared to other algorithms in 90% of test instances.
  • Performance was evaluated using inverted generational distance (IGD) and hypervolume (HV) indicators.

Conclusions:

  • The proposed IM-MFEA effectively reduces the negative impact of knowledge transfer in multi-task optimization.
  • The combination of inverse model mapping and objective transformation significantly improves optimization performance.
  • IM-MFEA offers a promising approach for tackling complex multi-objective multi-task optimization problems.