A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

The scientific field of optimization has witnessed an increasing trend in the development of metaheuristic algorithms within the current decade. The vast majority of the proposed algorithms have been proclaimed as superior and highly efficient compared to their contemporary counterparts by their own developers, which should be verified on a set of benchmark cases if it is to give conducive insights into their true capabilities. This study completes a comprehensive investigation of the general optimization capabilities of the recently developed nature-inspired metaheuristic algorithms, which have not been thoroughly discussed in past literature studies due to their new emergence. To overcome this deficiency in the existing literature, optimization benchmark problems with different functional characteristics will be solved by some of the widely used recent optimizers. Unconstrained standard test functions comprised of thirty-four unimodal scalable optimization problems with varying dimensionalities have been solved by these competitive algorithms, and respective estimated solutions have been evaluated relying on the performance metrics defined by the statistical analysis of the predictive results. Convergence curves of the algorithms have been construed to observe the evolution trends of objective function values. To further delve into comprehensive analysis on unconstrained test cases, CEC 2013 problems have been considered for comparison tools since their resemblances of the following features of real-world complex algorithms. The optimization capabilities of eleven metaheuristics algorithms have been comparatively analyzed on twenty-eight multidimensional problems. Finally, fourteen complex engineering problems have been optimized by the algorithms to scrutinize their effectiveness on handling the imposed design constraints.