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DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm
Shihong Yin1,2,3, Qifang Luo1,2,3, Yanlian Du4,5
1College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China.
Abstract:
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.
Insights
This study introduces a novel slime mould algorithm (SMA) enhancement, the dominant swarm with adaptive t-distribution mutation (DTSMA), improving convergence and balancing exploration for better optimization results.
Area of Science:
- Computational Intelligence
- Metaheuristic Optimization
- Swarm Intelligence
Background:
- The slime mould algorithm (SMA) is a recent metaheuristic inspired by slime mould behavior.
- Existing SMA suffers from imbalanced exploration-exploitation and a tendency towards local optima.
Purpose of the Study:
- To propose an improved slime mould algorithm (SMA) named DTSMA.
- To enhance convergence speed, exploration-exploitation balance, and population diversity.
Main Methods:
- Introduced dominant swarm for improved convergence speed.
- Implemented adaptive t-distribution mutation for enhanced exploration-exploitation.
- Integrated a new exploitation mechanism to increase population diversity.
Main Results:
- DTSMA demonstrated superior performance on CEC2019 benchmark functions.
- DTSMA achieved better results than SMA and other algorithms on engineering design problems.
- DTSMA effectively solved the inverse kinematics problem for a 7-DOF robot manipulator.
Conclusions:
- DTSMA shows significant improvements over the standard SMA.
- The proposed DTSMA is a promising metaheuristic for global optimization challenges.
- DTSMA offers enhanced optimization capabilities for complex problems.
