Evolutionary Algorithms Performance Comparison For Optimizing Unimodal And Multimodal Test Functions

Many evolutionary algorithms have been presented in the last few decades, some of these algorithms were sufficiently tested and used in many researches and papers, such as: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution Algorithm (DEA). Other recently proposed algorithms were unknown and rarely used such as Stochastic Fractal Search (SFS), Symbiotic Organisms Search (SOS), and Grey Wolf Optimizer (GWO). This paper trying to made a fair comprehensive comparison for the performance of these well-known algorithms and other less prevalent and recently proposed algorithms, by using a variety of famous test functions that have multiple different characteristics, through applying two experiments for each algorithm according to the used test function, the first experiments carried out with the standard search space limits of the proposed test functions, while the second experiment multiple ten times the maximum and minimum limits of the test functions search space, recording the Average Mean Absolute Error (AMAE), Overall Algorithm Efficiency (OAE), Algorithms Stability (AS), Overall Algorithm Stability (OAS), each algorithm required Average Processing Time (APT), and Overall successful optimized test function Processing Time (OPT) for both of the experiments, and with ten epochs each with 100 iterations for each algorithm.