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

Abstract

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