OMFO: A New Opposition-Based Moth-Flame Optimization Algorithm for Solving Unconstrained Optimization Problems

摘要

The Moth-Flame Optimization (MFO) algorithm is a nature-inspired search algorithm that has delivered good performance and efficiency in solving various optimization problems. In order to avoid local optimum and increase global exploration, each moth of MFO updates its position with respect to a specific MFO operation. However, MFO tends to suffer from a slow convergence speed and produces a low quality solution. This paper presents a new opposition-based scheme and embeds it into the MFO algorithm. The proposed algorithm is called OMFO. The experiments were conducted on a set of commonly used benchmark functions for performance evaluation. The proposed OMFO was compared with the original MFO and four other well-known algorithms, namely, PSO, DE, GSA and GWO. The results clearly showed that OMFO outperformed MFO and the four other algorithms used.