Investigating the actual performance of recent meta-heuristic algorithms in solving different optimization problems

Abstract

In this paper, the Egret Swarm Optimization Algorithm (ESOA) and Zebra Optimization Algorithm (ZOA) are executed to solve different optimization problems. The results achieved by the two algorithms are evaluated using different criteria, such as stability, minimum, average, and maximum convergence. The evaluation of the results indicates that ESOA not only maintains a surprising stability through its execution but also provides a faster response time compared to ZOA. ESOA requires at most 20 iterations to reach the best value of the main objective functions of the first two selected optimization problems, while ZOA cannot. In the last two selected optimization problems, ESOA continuously shows its superiority over ZOA with its high stability and low utilization of iterations to reach the best value of the main objective functions. Considering these results, ESOA deserves powerful search methods, and the method is strongly recommended to optimize such optimization problems.