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An improved differential evolution algorithm is given. In the algorithm a logarithm increased crossover and a random migration operator are used to overcome the convergent slowness in the later period of the iteration and fall easily into premature convergence. It is shown by the experiments on eight typical problems that the modified algorithm has strongly global search ability.
Differential evolution (DE) is a simple but efficient algorithm for the global optimization over continuous spaces. However, the problem of premature convergence still exists. When trapped in evolution stagnation, DE usually requires much time to jump over. In this paper, the algorithm of DE/rand/1/bin is improved by making use of the historical knowledge. An auxiliary population (AP) is used as a...
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