PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems

The development of the meta-heuristic algorithms for solving the optimization problems and constrained engineering problems is one of the topics of interest to researchers in recent years. Particle swarm optimization algorithm (PSO) is one of the social search-based and swarm intelligence algorithms that is distinguished by its high speed, low number of parameters and easy implementation. However, the PSO algorithm has disadvantages such as finding the local minimum instead of the global minimum and debility in global search capability. In this article, in order to solve these deficiencies, the PSO algorithm is combined with position updating equations in Sine Cosine Algorithm (SCA) and the Levy flight approach. Therefore, a new hybrid method called PSOSCALF is introduced in this paper. In the SCA algorithm, the mathematical formulation for the solution updating is based on the behavior of sine and cosine functions. These functions guarantee the exploitation and exploration capabilities. Levy flight is a random walk that produces search steps using Levy distribution and then, with large jumps, more effective searches are occurred in the search space. Thus, using combination of the SCA and Levy flight in the PSOSCALF algorithm, the exploration capability of the original PSO algorithm is enhanced and also, being trapped in the local minimum is prevented. The performance and accuracy of the PSOSCALF method have been examined by 23 benchmark functions of the unimodal and multimodal type and 8 constrained real problems in engineering. The optimization results of the test functions show that the PSOSCALF method is more successful than the PSO family and other algorithms in determining global minimum of these functions. Also, the proposed PSOSCALF algorithm is successfully applied to the real constrained engineering problems and provides better solutions than other methods. (C) 2018 Elsevier B.V. All rights reserved.