Open AccessDTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm
Author(s) -
Shihong Yin,
AUTHOR_ID,
Qifang Luo,
Yanlian Du,
Yongquan Zhou,
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AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022105
Subject(s) - swarm behaviour , metaheuristic , mathematical optimization , sma* , adaptive mutation , convergence (economics) , computer science , mutation , algorithm , distribution (mathematics) , inverse kinematics , mathematics , robot , artificial intelligence , genetic algorithm , mathematical analysis , biochemistry , chemistry , economics , gene , economic growth
The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, and easy to fall into local optimum. This paper, an improved SMA based on dominant swarm with adaptive t-distribution mutation (DTSMA) is proposed. In DTSMA, the dominant swarm is used improved the SMA's convergence speed, and the adaptive t-distribution mutation balances is used enhanced the exploration and exploitation ability. In addition, a new exploitation mechanism is hybridized to increase the diversity of populations. The performances of DTSMA are verified on CEC2019 functions and eight engineering design problems. The results show that for the CEC2019 functions, the DTSMA performances are best; for the engineering problems, DTSMA obtains better results than SMA and many algorithms in the literature when the constraints are satisfied. Furthermore, DTSMA is used to solve the inverse kinematics problem for a 7-DOF robot manipulator. The overall results show that DTSMA has a strong optimization ability. Therefore, the DTSMA is a promising metaheuristic optimization for global optimization problems.