MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy

Moth-flame optimization (MFO) is a prominent problem solver with simple structure that widely used to solve different problems. However, MFO and its variants inherently suffer from poor population diversity, leading premature convergence local optima losses in the quality of solutions. To overcome these limitations, an enhanced moth-flame algorithm named MFO-SFR was developed global The introduces effective stagnation finding replacing (SFR) strategy effectively maintain diversity throughout process. SFR can find stagnant solutions using distance-based technique replaces them selected solution archive constructed previous effectiveness proposed extensively assessed 30 50 dimensions CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, composition Then, obtained results were compared two sets competitors. In first comparative set, well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, WMFO, considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, HOA, considered second set. then statistically analyzed through Friedman test. Ultimately, capacity mechanical engineering problems evaluated latest 2020 test-suite. experimental statistical analysis confirmed superior algorithms for solving complex problems, 91.38% effectiveness.