Abstract The goal of optimizing the best acceptable answer is according to the limitations and needs of the problem. For a problem, there are several different answers that are defined to compare them and select an optimal answer; a function is called a target function. The choice of this function depends on the nature of the problem. Sometimes several goals are together optimized; such optimization problems are called multi-objective issues. One way to deal with such problems is to form a new objective function in the form of a linear combination of the main objective functions. In the proposed approach, in order to increase the ability to discover new position in the Imperialist Competitive Algorithm (ICA), its operators are combined with the particle swarm optimization. The colonial competition optimization algorithm has the ability to search global and has a fast convergence rate, and the particle swarm algorithm added to it increases the accuracy of searches. In this approach, the cosine similarity of the neighboring countries is measured by the nearest colonies of an imperialist and closest competitor country. In the proposed method, by balancing the global and local search, a method for improving the performance of the two algorithms is presented. The simulation results of the combined algorithm have been evaluated with some of the benchmark functions. Comparison of the results has been evaluated with respect to metaheuristic algorithms such as Differential Evolution (DE), Ant Lion Optimizer (ALO), ICA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

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[28] Zheng L., Zou J., Liu B., Hu Y., and Deng Y., “A Novel Evidence Distance in Power Set Space,” The International Arab Journal of Information Technology, vol. 17, no. 1, pp. 8-15, 2020. Improvement of Imperialist Competitive Algorithm based on the Cosine Similarity ... 269 Maryam Houtinezhad completed her B.Sc. degree in computer engineering at Azad Islamic University, her M.Sc. degree at the University of Science and Research, and her Ph.D. at the Islamic Azad University, Ferdows Branch. Her interest research areas are image processing, pattern recognition and the application of optimization and web mining algorithms. Hamid Reza Ghaffary completed his B.Sc. degree in computer science at Sharif University of Technology and his M.Sc. degree at the University of South Tehran and his Ph.D. at Ferdowsi University. He is currently a faculty member and assistant professor at the Faculty of Computer Engineering, Ferdows Azad University. His interest research areas are machine learning, pattern recognition and image processing.