.png){kind=icon}
返回
Multi-swarm improved moth-flame optimization algorithm with chaotic grouping and Gaussian mutation for solving engineering optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.117562.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Moth-Flame Optimization (MFO) is widely utilized to solve optimization problems in different fields since it has a simple structure and easy implementation. However, MFO cannot effectively balance exploration and exploitation and often suffers from the lack of population diversity in the search process, especially in solving some complex engineering optimization problems. To overcome the above problems, in this paper, a multiswarm improved moth-flame algorithm (MIMFO) is proposed. In MIMFO, firstly, the population is grouped and dynamically reorganized through chaotic grouping mechanism and dynamic regrouping mechanism, which can improve the grouping quality and diversity of the population. Secondly, spiral search and linear search are carried out for the two sub-swarms to improve the search efficiency and balance exploration and exploitation. In addition, Gaussian mutation is used to generate flame, which can accelerate convergence and enhance the exploration. The MIMFO is verified on 13 benchmark problems with 30, 500, 1000, 2000 dimensions and CEC 2014 test functions. The results show that the MIMFO is superior to other swarm intelligence algorithms and MFO variants in finding the global optimum and convergence performance. Finally, MIMFO is used to solve 57 engineering constraint optimization problems, and the results show that MIMFO can solve real-world engineering problems.
Keyword:
Moth-flame optimization algorithm
Multi-swarm
Gaussian mutation
Linear search
Engineering optimization problems
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏Golden sine cosine salp swarm algorithm for shape matching using atomic potential function.png)
EXPERT SYSTEMS
EXPERT SYSTEMSIF2.3
PREAI
分享收藏分享收藏
查看更多内容