An enhanced Moth-Flame optimizer with quality enhancement and directional crossover: optimizing classic engineering problems

Abstract

As a popular meta-heuristic algorithm, the Moth-Flame Optimization (MFO) algorithm has garnered significant interest owing to its high flexibility and straightforward implementation. However, when addressing engineering constraint problems with specific parameters, MFO also exhibits limitations such as fast convergence and a tendency to converge to local optima. In order to address these challenges, this paper introduces an enhanced version of the MFO, EQDXMFO. EQDXMFO integrates a Quality Enhancement (EQ) strategy and a Directional Crossover (DX) mechanism, fortifying the algorithm’s search dynamics. Specifically, the DX mechanism is designed to augment the population’s diversity, enhancing the algorithm’s exploratory potential. Concurrently, the EQ strategy is employed to elevate the solution quality, which in turn refines the convergence precision of the algorithm. To verify the effectiveness of EQDXMFO, experiments are carried out on the test set of the IEEE CEC2017. A total of 5 classical algorithms, five excellent MFO variants, and seven state-of-the-art algorithms are selected for comparison, which confirm the significant advantages of EQDXMFO. Next, EQDXMFO is applied to five complex engineering constraint problems, demonstrating that EQDXMFO can optimize realistic problems. The comprehensive analysis shows that EQDXMFO has strong optimization capabilities and provides methods for research on other complex real-world problems.