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Self-adaptive differential evolution algorithm using population size reduction and three strategies
Brest, Janez ; Sepesy Maučec, Mirjam
Many real-world optimization problems are largescale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the ...problemsć 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. Nonparametric statistical procedures were performed formultiple comparisons between the proposed algorithm and three wellknown 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.
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