Solving constrained optimization problems with sine-cosine algorithm

Abstract

Optimization algorithms aim to find the optimum values that give the maximum or minimum result of a function under given circumstance. There are many approaches to solve optimization problems. Stochastic population-based optimization approaches tend to give the best results in a reasonable time. Two of the state-of-art stochastic optimization algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). In addition, Sine-Cosine Algorithm is one of the recently developed stochastic population-based optimization algorithms. It is claimed that Sine-Cosine has a higher speed than the counterparts of it. Moreover, Sine-Cosine Algorithm occasionally outperforms other optimization algorithms including GA and PSO. This algorithm is successful because it can balance exploration and exploitation smoothly. In the previous studies, the above-mentioned algorithms were evaluated and compared to each other for the unconstrained optimization test functions. But there is no study on constrained optimization test problems. In this study, we aim to show the performance of Sine-Cosine Algorithm on constrained optimization problems. In order to achieve this, we are going to compare the performances by using well-known constrained test functions.