Self-adaptive differential evolution algorithm using population size reduction and three strategies

Authors:
Janez Brest;Mirjam Sepesy Maučec
Affiliations:
University of Maribor, Faculty of Electrical Engineering and Computer Science, Smetanova ulica 17, 2000, Maribor, Slovenia;University of Maribor, Faculty of Electrical Engineering and Computer Science, Smetanova ulica 17, 2000, Maribor, Slovenia
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Year:
2011

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Abstract

Many real-world optimization problems are large-scale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problems’ particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algorithm employs three strategies and a population size reduction mechanism. The performance of the jDElscop algorithm is evaluated on a set of benchmark problems provided for the Special Issue on the Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. Non-parametric statistical procedures were performed for multiple comparisons between the proposed algorithm and three well-known algorithms from literature. The results show that the jDElscop algorithm can deal with large-scale continuous optimization effectively. It also behaves significantly better than other three algorithms used in the comparison, in most cases.