Journal of Global Optimization
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
Two improved differential evolution schemes for faster global search
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
IEEE Transactions on Evolutionary Computation
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In Differential Evolution, control parameters play important roles in balancing the exploration and exploitation capability, and different control parameters are required for different types of problems. However, finding optimal control parameters for each problem is difficult and not realistic. Hence, we propose a method to adjust them adaptively in this paper. In our proposed method, whether or not the current control parameters will be adjusted is based on a probability that is adaptively calculated according to their previous performance. Besides, normal distribution with variable mean value and standard deviation is employed to generate new control parameters. Performance on a set of benchmark functions indicates that our proposed method converges fast and achieves competitive results.