A Proposed Gray Wolf Optimization Combining with Shuffled Complex Evolution

In many complex optimization problems, finding the optimal solution can be very difficult. In such problems, finding a good solution that is near the optimal consider the task of optimization algorithms. Metaheuristic optimization algorithms try to find good solutions to complex problems in various fields and most of them are inspired by nature. In this paper, a metaheuristic hybrid algorithm was proposed. The algorithm combines Shuffled Complex Evolution (SCE) with Gray Wolf Optimizer (GWO). GWO is an optimization algorithm and its efficiency has been proven in many fields such as medicine and engineering. However, GWO has a drawback: the possibility of falling into the local minima because of the lack of diversity. To overcome this drawback; GWO was combined with SCE. The GWO was used in the solutions of each complex and SCE works to exchange information between complexes by compiling them and reorganizing them into new complexes. In this way, the global search was improved through the modification of the local search by using this hybrid combination. The performance of this hybrid approach was tested using ten benchmark functions and compared with several conventional algorithms. The results indicate that the proposed algorithm can give competitive and more consistent results compared to the conventional algorithms. The algorithm was also applied for medical image fusion and gave a better result than those obtained from GWO and CS alone.