Multi-Strategy-Improvement-Based Slime Mould Algorithm
Source abstract
Abstract
In addressing the challenges posed by the sluggish convergence rate, suboptimal stability, and susceptibility to local optimization in function optimization problems, a multi-strategy-based enhanced slime mold optimization algorithm (MSSMA) has been proposed. This algorithm integrates chaotic mapping and inverse learning to enhance the convergence speed of the initial population. Additionally, a novel balancing factor, B, has been introduced to ensure a more equitable distribution of the algorithm’s exploration and exploitation. The enhanced Lévy flight strategy and the elite tangent search strategy have been integrated to further enhance the algorithm’s global search capability and optimization finding ability. The simulation experiments have demonstrated that the enhanced algorithm exhibits faster convergence speed, enhanced stability, and a superior ability to escape local optima when compared to the other five algorithms in 50 benchmark test functions and multi-UAV cooperative path planning scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Scheduling and Optimization Algorithms · Industrial Technology and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Funding
National Natural Science Foundation of China
References (33)
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