Multi-swarm improved moth–flame optimization algorithm with chaotic grouping and Gaussian mutation for solving engineering optimization problems

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 proc...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Expert systems with applications Jg. 204; S. 117562
Hauptverfasser: Zhao, Xiaodong, Fang, Yiming, Ma, Shuidong, Liu, Zhendong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.10.2022
Schlagworte:
Engineering optimization problems
Gaussian mutation
Linear search
Moth–flame optimization algorithm
Multi-swarm
Engineering optimization problems
Multi-swarm
Linear search
Gaussian mutation
Moth–flame optimization algorithm
ISSN:0957-4174
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 multi-swarm 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. •A multi-swarm improved moth–flame algorithm (MIMFO) is proposed.•Chaotic grouping and dynamic regrouping are used to improve population diversity.•Two sub-swarms are searched by spiral search and line search respectively.•Gaussian mutation is used to mutate the optimal flame.•43 test problems and 57 engineering problems are used to evaluate the MIMFO.
ISSN:0957-4174
DOI:10.1016/j.eswa.2022.117562