An Improved Moth-Flame Optimization Algorithm for Engineering Problems

Abstract

In this paper, an improved moth-flame optimization algorithm (IMFO) is presented to solve engineering problems. Two novel effective strategies composed of Lévy flight and dimension-by-dimension evaluation are synchronously introduced into the moth-flame optimization algorithm (MFO) to maintain a great global exploration ability and effective balance between the global and local search. The search strategy of Lévy flight is used as a regulator of the moth-position update mechanism of global search to maintain a good research population diversity and expand the algorithm’s global search capability, and the dimension-by-dimension evaluation mechanism is added, which can effectively improve the quality of the solution and balance the global search and local development capability. To substantiate the efficacy of the enhanced algorithm, the proposed algorithm is then tested on a set of 23 benchmark test functions. It is also used to solve four classical engineering design problems, with great progress. In terms of test functions, the experimental results and analysis show that the proposed method is effective and better than other well-known nature-inspired algorithms in terms of convergence speed and accuracy. Additionally, the results of the solution of the engineering problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces.