* Corresponding author E-mail: omerryilmaz@gmail.com (Ö. Yılmaz) 2022 Growing Science Ltd. doi: 10.5267/j.ijiec.2021.11.001 International Journal of Industrial Engineering Computations 13 (2022) 237–254 Contents lists available at GrowingScience International Journal of Industrial Engineering Computations homepage: www.GrowingScience.com/ijiec A new hybrid algorithm based on MVO and SA for function optimization Ömer Yılmaz a* , Adem Alpaslan Altun b and Murat Köklü b a Department of Information Technologies, Tokat Vocational and Technical Anatolian High School, 60100, Tokat, Turkey b Department of Computer Engineering, Faculty of Technology, Konya Selcuk University, 42130, Konya, Turkey C H R O N I C L E A B S T R A C T Article history: Received May 11 2021 Received in Revised Format June 28 2021 Accepted October 22 2021 Available online October, 27 2021 Hybrid algorithms are widely used today to increase the performance of existing algorithms. In this paper, a new hybrid algorithm called IMVOSA that is based on multi-verse optimizer (MVO) and simulated annealing (SA) is used. In this model, a new method called the black hole selection (BHS) is proposed, in which exploration and exploitation can be increased. In the BHS method, the acceptance probability feature of the SA algorithm is used to increase exploitation by searching for the best regions found by the MVO algorithm. The proposed IMVOSA algorithm has been tested on 50 benchmark functions. The performance of IMVOSA has been compared with other latest and well-known metaheuristic algorithms. The consequences show that IMVOSA produces highly successful and competitive results. © 2022 by the authors; licensee Growing Science, Canada Keywords: Simulated annealing Multi-verse optimizer Hybrid optimization algorithm Function optimization 1. Introduction Optimization is defined as the process of finding the best solution among alternative solutions in line with the conditions given for a specific problem. The basic goal of the optimization method is to find the necessary parameters for the best result of the fitness function (Murty, 2003). Due to the tremendous recent development of information technology, the use of optimization methods has increased. Many real-world problems can be seen as optimization problems and many algorithms have been used to solve optimization problems. Metaheuristic algorithms are the popular algorithms that are used for solving optimization problems. Metaheuristic algorithms aim to examine the search space effectively and efficiently in optimization problems where the mathematical model cannot be established or where it is very costly to build a model. Although it is not always possible to find the best global solution with these algorithms, the convenience of their application, their ability to produce fast and effective solutions to large-scale and complex problems, the fact that the metaheuristic method developed for any problem can also be applied to other problems makes these methods very useful (Kaya & Fığlalı 2018; Talbi, 2009). The most important advantage of the metaheuristic algorithm can be said to be the ability to reach the global best without getting stuck with the local best (Laporte et al., 2000). Considering the publications, there are various metaheuristic algorithms that have been used and accepted in many studies. Differential Evolution (DE) (Storn, 1996; Storn & Price, 1997), Ant Colony Optimization (ACO) (Colorni et al., 1991; Jovanovic & Tuba, 2013), Artificial Bee Colony (ABC) (Karaboga, 2005), Gravity Search Algorithm (GSA) (Rashedi et al., 2009), Cat Swarm Optimization (CSO) (Chu et al., 2006), Animal Migration Optimization (AMO) (Li et al., 2014; Luo et al., 2016), Particle Swarm Optimization (PSO) (Kennedy & Eberhart, 1995), Simulated Annealing (SA) (Kirkpatrick et al., 1983), Harris Hawks Optimization (HHO) (Heidari et al., 2019), Multi-verse Optimizer (MVO) (Mirjalili et al., 2016) algorithms can be given as examples of metaheuristic algorithms.