Population Reduction Differential Evolution with Multiple Mutation Strategies in Real World Industry Challenges

This paper presents a novel differential evolution algorithm for optimization of state-of-the-art real world industry challenges. The algorithm includes the self-adaptive jDE algorithm with one of its strongest extensions, population reduction, and is now combined with multiple mutation strategies. The two mutation strategies used are run dependent on the population size, which is reduced with growing function evaluation number. The problems optimized reflect several of the challenges in current industry problems tackled by optimization algorithms nowadays. We present results on all of the 22 problems included in the Problem Definitions for a competition on Congress on Evolutionary Computation (CEC) 2011. Performance of the proposed algorithm is compared to two algorithms from the competition, where the average final best results obtained for each test problem on three different number of total function evaluations allowed are compared.