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
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Note on the Extended Rosenbrock Function
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential evolution and non-separability: using selective pressure to focus search
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel set-based particle swarm optimization method for discrete optimization problems
IEEE Transactions on Evolutionary Computation
Pheromone-distribution-based adaptive ant colony system
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
IEEE Transactions on Evolutionary Computation
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The performance of differential evolution (DE) largely depends on an appropriate selection of the values of the algorithmic parameters. Usually, it is difficult to choose optimal parameter values, because they are often ad hoc to the specific problem in question and also related to the optimization states that the DE is in during its search process. In this paper, a novel adaptive parameter control scheme is proposed for DE. Improving from existing parameter control schemes, the parameters F and CR in DE are adaptively controlled according to the optimization states, namely, exploration state and exploitation state in each generation. These optimization states are estimated by measuring the population distribution. During the optimization process of DE, the distribution of population varies and reflects the search maturity. In the exploration state, individuals in the population distribute evenly in the search space. As the optimization matures, the population gradually converges on a global or local optimum in the exploitation state. This feature enables parameter adaptation with a fuller utilization of the prevailing optimization information and hence reduces inappropriate adjustments. The proposed adaptive parameter control scheme is applied to the famous DE/rand/1 algorithm. Experimental results show that this scheme can effectively improve the efficiency and robustness of the algorithm.