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Multipopulation-based multi-tasking evolutionary algorithm

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Abstract

Multi-tasking optimization (MTO) has attracted more and more attention from researchers in the area of evolutionary computing. The main factor affecting the success of MTO is knowledge transfer. Nevertheless, knowledge transfer between tasks has positive and negative effects on tasks that are solved simultaneously. In multi-task evolutionary optimization, the negative migration can be suppressed to a certain extent by adjusting random mating probability between tasks, but the negative migration between tasks cannot be completely avoided. This paper proposes a new multi-population-based multi-task evolutionary algorithm (MPEMTO) to weaken the impact of negative knowledge transfer between tasks. The MPEMTO has a novel dual information transfer strategy, an adaptive knowledge screening mechanism, an extended adaptive mating strategy, and a computational resource allocation method. MPEMTO first applies adaptive mating strategy and dual information migration strategy to control the transfer of knowledge between tasks and then applies a transfer information screening mechanism to screen the transfer information to achieve effective use of the transfer information between tasks. The effectiveness of MPEMTO is compared with eight excellent algorithms on single-object MFO test problems. The experimental results demonstrate that the performance of the MPEMTO algorithm is very competitive on most optimization problems.

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  1. http://www.bdsc.site/websites/MTO/index.html.

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Acknowledgements

The authors would like to express their sincere thanks to Professor Gao Wei feng for providing the source code of MFMPSHADE. This work is supported by the National Natural Science Foundation of China under Grant (No⋅62176146, No⋅61773314, No⋅61803301), Shaanxi Provincial Natural Science Basic Research Program under Grant No.2019JZ-11, Scientific Research Project of Education Department of Shaanxi Provincial Government under Grant No⋅19JC011.

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Authors and Affiliations

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

    Xiaoyu Li, Lei Wang & Qiaoyong Jiang

  2. School of Electronic and Information Engineering, Ankang University, Ankang, 725000, China

    Xiaoyu Li

  3. The Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, 723001, China

    Lei Wang

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  1. Xiaoyu Li
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Correspondence to Lei Wang.

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Li, X., Wang, L. & Jiang, Q. Multipopulation-based multi-tasking evolutionary algorithm. Appl Intell 53, 4624–4647 (2023). https://doi.org/10.1007/s10489-022-03626-w

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