A novel electrons drifting algorithm for non-linear optimization problems

In response to higher and higher dimensions and complexity of optimization problems in engineering applications, the optimization algorithms face more and more challenges. This paper proposes a novel electron drifting algorithm (e-DA) to avoid the common disadvantages, such as easy to trap in a local optimal point and sensitive to initial solutions, of existing methods. A simple example is addressed in the paper to make readers easily understand the executed processes. Some benchmark functions are used for testing the effectiveness of the proposed e-DA. Besides, the performance of e-DA is compared with the existing optimization algorithms, including particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony (ABC). Numerical results verify that the searching efficiency and capability of the proposed e-DA are enhanced and better than the existing algorithms.