An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization

Differential evolution (DE) is a simple, fast, and efficient evolutionary algorithm for global numerical optimization. The major advantage of DE is its self-adaptation in both search direction and step size of the differential mutation.. On the other hand, DE is good at exploring. The search space and locating the region of global minimum, but it is slow at exploitation of the solution. In order to alleviate these drawbacks of DE, in this paper, we propose an improved self-adaptive control parameter of DE, referred to as ISADE, for global numerical optimization. In order to verify the performance of our approach, ten widely used benchmark functions and the results are compared with the original DE algorithm. Experimental results verify our expectation that the search parameters controlled DE algorithm obtains better results than the original DE algorithm in term of the solution quality and convergence rate. Our approach performs better than the original DE in terms of both the convergence rate and the quality of the final solutions. Moreover, ISADE obtains faster convergence rate than the original self-adaptive control parameter of DE (SADE).