An Ensemble of Differential Evolution Algorithms for Constrained Function Optimization

Abstract

This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrained optimization problems. It is well known that the performance of DE is sensitive to the choice of mutation strategies and associated control parameters. For these reasons the ensemble is achieved in such a way that each individual is assigned to one of the two distinct mutation strategies or a variable parameter search (VPS). The algorithm was tested using benchmark instances in Congress on Evolutionary Computation 2010. For these benchmark problems the problem definition file codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since the optimal or best known solutions are not available in the literature the detailed computational results required in line with the special session format are provided for the competition.