A hybrid multi-objective wind driven optimization algorithm

Basically, optimization is the process of finding the best solutions for one or more problems under certain conditions. Today, classical methods and heuristic methods are used to solve these problems. Wind Driven Optimization algorithm, which is one of the heuristic methods, is an algorithm that seeks solutions for single-objective optimization problems that benefit from atmospheric dynamic equality based on the movement of the wind in the atmosphere. In this study, Wind Driven Optimization algorithm was rearranged to solve multi-objective optimization problems. In multi-objective optimization problems, the performance of the used method depends on how closely the results obtained converge to the actual results and how diverse these results are. Methods such as non-dominant sorting, weighted sum, normal boundary intersection are frequently used approaches in multi-objective optimization problems. While some of these approaches have more diverse to results, others have been observed to better converge. The main purpose of this study is to optimize the solutions obtained in terms of both diversity and convergence. Within this scope, a new hybrid approach has been developed by using non-dominant sorting and adaptive grid approaches. Non-dominant sorting used for better convergence and adaptive grid approach is used for diversity. The developed hybrid approach has been tested in test problems and nonlinear equation systems. Results have been compared with Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). When the experimental results were examined, it was observed that the hybrid approach developed in terms of diversity and convergence performance was acceptable.