Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (6): 98-108.doi: 10.16088/j.issn.1001-6600.2021122104

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An Improved Slime Mould Algorithm Based on Multi-Strategy

WANG Ximin, YUAN Jie*, KOU Qiaoyuan   

  1. School of Electrical Engineering, Xinjiang University, Urumqi Xinjiang 830017, China
  • Received:2021-12-21 Revised:2022-03-09 Online:2022-11-25 Published:2023-01-17

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Abstract: Aiming at the problem of low search efficiency and local optimum of Slime Mould Algorithm (SMA), an improved multi-strategy Slime Mould Algorithm is proposed. Firstly, the optimal population is obtained by Tent mapping reverse learning strategy as the initial population to improve the convergence speed of the algorithm. Secondly, slime molds updates location through adaptive weight strategy and disturbance strategy, adjusts the exploration and development ability of the algorithm, avoids falling into premature, and improves the convergence speed. Finally, compared with four classical algorithms including PSO, WOA, GWO, SMA and a related improved SMA algorithm, CEC test function is tested. The optimization results show that the improved algorithm has strong search efficiency and ability to avoid falling into local optimum. The algorithm can find the global optimal value in a short period of time, which are more effective and can improves the convergence speed and convergence accuracy of the test function to some degrees.

Key words: Tent map, reverse learning strategy, adaptive weight strategy, perturbation strategy, slime molds algorithm

CLC Number: 

  • TP18
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