Multi-Purpose Metaheuristic Optimization Performance Mixing of Algorithms

Abstract

Optimization is the process of producing appropriate solutions for the purposes of unconstrained or constrained problems. Various optimization algorithms have been developed to realize the optimization process. As single-objective optimization algorithms are inadequate for problems with more than one purpose in daily life, multiobjective optimization algorithms have been developed. In the developed algorithms, various methods have been used to find the most suitable solution set. The most effective of these methods is the pareto optimal method which is widely used. The pareto optimal set of solutions achieved by multi -objective optimization in the Pareto optimal method includes all the best solutions at certain intervals, not the solutions of the problems at a single point. In this study, performance comparison of Multi-Objective Ant Lion Optimization Algorithm and Multi -Objective Dragonfly Algorithm on current comparative functions and engineering problems were compared.