Martial Art Learning Optimization: A Novel Metaheuristic Algorithm for Night Image Enhancement

Martial Art Learning Optimization: A Novel Metaheuristic Algorithm for Night Image Enhancement

This paper proposes a human behavior-based optimization algorithm, Martial Arts Learning Optimization (MALO), for optimization problems in continuous spaces. The algorithm simulates the process of characters in martial arts learning so as to apply it to optimization problems. Characters in martial arts stories usually go through multiple stages of learning martial arts, such as self-study and leader teaching. Multiple learning stages of characters are modeled in this paper, utilizing the wisdom of the characters learning martial arts in the novel, enabling the optimization process. To verify and analyze the performance of the proposed algorithm, the algorithm is numerically tested on 30 benchmark functions, and it is found that its performance was better than the state-of-the-art nine algorithms. In addition, the algorithm is also used to solve the problem of nighttime image brightness enhancement. Compared with other image enhancement methods, the proposed MALO algorithm has superior results in both visual effects and quantitative image quality assessments.