A sine cosine mutation based differential evolution algorithm for solving node location problem

Differential Evolution (DE) algorithm is known in evolutionary computation. However, DE with DE/best/1 mutation has some drawbacks such as premature convergence and local optimum. To address these drawbacks, we improve the DE/best/1 mutation operator and propose a sine cosine mutation based differential evolution algorithm, named SCDE. In the proposed method, a new sine cosine mutation operator inspired by sine cosine algorithm (SCA) is adopted to balance exploration and exploitation. In the experimental simulation, the proposed algorithm is compared with three state-of-The-Art algorithms on the well-known benchmark test functions. The results of test functions and performance metrics show that the proposed algorithm is able to avoid local optima and converge towards the global optimum. In addition, the proposed algorithm is used to solve sensor node location in wireless sensor network. Results show that our algorithm is effective. © 2017 Inderscience Enterprises Ltd.