Towards Improving Self-adaptive Differential Evolution for Numerical Optimization: A Progress Note

Differential evolution (DE) is a popular, simple, fast, efficient and stochastic optimization technique which shows a solid performance for diverse continuous optimization problems. However, the control parameters used in DE are fixed. This fact motivated many researchers in past few years to present improved variants of DE. It has been done by (1) modifying the self structure of DE, (2) integrating additional components within the structure of DE. For each category, several algorithms have been reported in the literature. Among of them, in this article, we have been selected four self structural modified adaptive variants of DE, called DE/best/1, DE with random scale factor, DE with time varying scale factor, modified differential evolution, after studying their working principles. Performance comparisons of all these algorithms are provided against the classical DE for ten numerical benchmark functions with the following performance measures: solution quality, number of generations required to find the optimal solution and frequency of finding the optimal solution. From the simulation results, it has been observed that the convergence speed of the recently proposed modified differential evolution is significantly better than others. Also statistical significance test has been carried out to establish the statistical significance of the results.