Abstract
Using appropriate tools to solve optimization problems is a major challenge in various sciences. In this paper, a new optimization algorithm called Three-Period Optimization Algorithm (TPOA) is designed to provide suitable quasi-optimal solutions for various optimization problems. The main idea in designing the proposed TPOA is to manage the updating of population members during replication of the algorithm. In this way, the total execution time of the algorithm is divided into three periods in which each period follows a specific update process for members of the population. In the first period, a composite member, in the second period, the worst member of the population, and in the third period, the best member of the population is used to update the members of the population. The proposed TPOA is mathematically modeled and implemented on a standard set of objective functions of unimodal and multimodal types. The optimization results indicate the optimal ability of the TPOA to solve various optimization problems and provide appropriate quasi-optimal solutions. To analyze the quality of the proposed TPOA, the results are compared with the performance of eight optimization algorithms, including Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), Gravitational Search Algorithm (GSA), and Genetic Algorithm (GA). Examining the simulation results and comparing the performance of optimization algorithms shows that the proposed TPOA has high power in solving optimization problems and is superior and much more competitive than the eight compared algorithms.