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 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.

Automated summary

A multiswarm improved moth-flame algorithm (MIMFO) is proposed in this paper, which enhances the moth-flame optimization with chaotic grouping and dynamic regrouping mechanisms. The results show that MIMFO outperforms other swarm intelligence algorithms and MFO variants in terms of global optimum and convergence performance.