Robust Optimization Over Time with Differential Evolution using an Average Time Approach

This paper presents an extension of a preliminary study about differential evolution in the solution of robust optimization over time problems. A set of test instances with four dynamics with three different time window values are solved by six differential evolution variants, whose parameters were set by means of a tool based on statistical methods so as to promote a fair comparison. The results are compared against one particle swarm optimization algorithm found as very competitive when solving robust optimization over time problems. The results obtained suggest that the most popular differential evolution variant, DE/rand/1/bin, is the most competitive when compared with the particle swarm optimization algorithm.