Differential evolution (DE) algorithm has been proven to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, which is widely used in both benchmark test functions and realworld applications. Like genetic algorithms, differential evolution algorithm uses three typical operators to search the solution space: crossover, mutation and selection. Among these operators, mutation plays a key role in the performance of differential evolution algorithm and there are several mutation variants often used, which constitute several corresponding differential evolution strategies. By means of experiments, this paper investigates the relative performance of different differential evolution algorithms for global optimization under different differential evolution strategies respectively. In simulation studies, De Jong's test functions have been employed, and some conclusions are drawn. Global Congress on Intelligent Systems978-0-7695-3571-5/09 $25.00

The Differential Evolution (DE) algorithm is arguably one of the most powerful stochastic optimization algorithms, which has been widely applied in various fields. Global numerical optimization is a very important and extremely dif-ficult task in optimization domain, and it is also a great need for many practical applications. This paper proposes an opposition-based DE algorithm for global numerical optimization, which is called GNO2DE. In GNO2DE, firstly, the opposite point method is employed to utilize the existing search space to improve the convergence speed. Secondly, two candidate DE strategies “DE/rand/1/bin” and “DE/current to best/2/bin” are randomly chosen to make the most of their respective advantages to enhance the search ability. In order to reduce the number of control parameters, this algorithm uses an adaptive crossover rate dynamically tuned during the evolutionary process. Finally, it is validated on a set of benchmark test functions for global numerical optimization. Compared with several existing algorithms, the performance of GNO2DE is superior to or not worse than that of these algorithms in terms of final accuracy, convergence speed, and robustness. In addition, we also especially compare the opposition-based DE algorithm with the DE algorithm without using the opposite point method, and the DE algorithm using “DE/rand/1/bin” or “DE/current to best/2/bin”, respectively

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Differential evolution (DE) has been shown to be a simple and effective evolutionary algorithm for global optimization both in benchmark test functions and many real-world applications. This paper introduces a dynamic differential evolution (D-DE) algorithm to solve constrained optimization problems. In D-DE, a novel mutation operator is firstly designed to prevent premature. Secondly, the scale factor F and the crossover probability CR are dynamic and adaptive to be beneficial for adjusting control parameters during the evolutionary process, especially, when done without any user interaction. Thirdly, D-DE uses orthogonal design method to generate initial population and reinitialize some solutions to replace some worse solutions during the search process. Finally, D-DE is validated on 6 benchmark test functions provided by the CEC 2006 special session on constrained real-parameter optimization. The experimental results obtained by D-DE are explained and discussed, and some conclusions are also drawn.