Evaluation of Advanced Jaya Algorithm in engineering design problems: a comparative study with recent metaheuristics

One of the most recent algorithms called Advanced Jaya Algorithm (A-JA) is a combination of evolutionary technique, swarm-based metaheuristic and population-based approach. In this study, A-JA is investigated as an optimization method for engineering problems found in the literature. To assess the capability of A-JA, ten design problems that have different mathematical models were employed. In addition, the same problems were solved using popular algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), as well as less familiar metaheuristics like Harris Hawk Optimization (HHO) and Grey Wolf Optimization (GWO). The results showed that A-JA produces efficient outcomes, such as the ability to establish an effective equilibrium between exploration and exploitation and to escape from local optimums. Thus, the results indicate that A-JA often reports better solutions than other optimization algorithms.