Optimization: A comparison of recent meta-heuristic optimization algorithms using benchmark function

Abstract

Metaheuristic optimization algorithms are used in many application areas to solve optimization problems. In recent decades, the popularity of metaheuristic optimization algorithms has increased compared to deterministic search algorithms in solving global optimization problems. But none of the algorithms can solve all optimization problems equally well. Therefore, researchers focus on either improving existing meta-heuristic optimization algorithms or introducing new algorithms. Many alternative metaheuristic algorithms inspired by nature have been developed to solve complex optimization problems. It is important to compare the performances of the developed algorithms through statistical analysis and determine the better algorithm. This paper compares the performances of sixteen proposed metaheuristic optimization algorithms (AWDA, MAO, TSA, TSO, ESMA, DOA, LHHO, DSSA, LSMA, AOSMA, AGWOCS, CDDO, GEO, BES, LFD, HHO) between 2021 and 2022. These algorithms have been tested with various test functions, including single-mode, multi-mode, and fixed-size multi-mode benchmark functions. Based on the obtained results, the success of the algorithms is visualized with the frequency histogram diagram.