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An adaptive multitasking optimization algorithm based on population distribution.

Xiaoyu Li1,2, Lei Wang1,3, Qiaoyong Jiang1

  • 1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.

Mathematical Biosciences and Engineering : MBE
|March 8, 2024
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Summary

This study introduces an adaptive evolutionary multitasking optimization (EMTO) algorithm. It uses population distribution and maximum mean discrepancy to improve knowledge transfer and reduce negative transfer between tasks, enhancing optimization performance.

Keywords:
differential evolutionevolutionary multitasking optimizationmaximum mean differencepopulation distribution informationsimilarity

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Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Evolutionary multitasking optimization (EMTO) enhances performance by transferring knowledge between tasks.
  • Identifying effective knowledge transfer and mitigating negative transfer are critical challenges in EMTO.
  • Existing methods often rely on elite solutions, which can be insufficient when task optima diverge significantly.

Purpose of the Study:

  • To develop an adaptive EMTO algorithm that leverages population distribution information for improved knowledge transfer.
  • To identify valuable knowledge and reduce negative transfer in multitasking optimization scenarios.
  • To enhance the effectiveness of EMTO, particularly when task global optima are distant.

Main Methods:

  • The proposed algorithm divides task populations into K sub-populations based on fitness.
  • Maximum Mean Discrepancy (MMD) is used to quantify distribution differences between sub-populations.
  • Selected sub-populations with minimal MMD are used for knowledge transfer, alongside an adaptive interaction probability.

Main Results:

  • The adaptive EMTO algorithm demonstrated high solution accuracy across tested multitasking problems.
  • The approach achieved fast convergence, especially for problems with low task relevance.
  • Experimental results validate the effectiveness of using population distribution for knowledge transfer.

Conclusions:

  • The proposed adaptive EMTO algorithm effectively identifies and transfers relevant knowledge while minimizing negative transfer.
  • Population distribution analysis and MMD provide a robust mechanism for selecting knowledge for transfer.
  • The algorithm offers a promising solution for improving EMTO performance, particularly in complex, low-relevance multitasking environments.