Journal of Global Optimization
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Adaptive Approaches for Differential Evolution
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Population size reduction for the differential evolution algorithm
Applied Intelligence
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Enhancing differential evolution frameworks by scale factor local search: part I
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Enhancing differential evolution frameworks by scale factor local search: part II
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
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For multi-dimensional function optimization problems, classical differential evolution (DE) algorithm may deteriorate its intensification ability because different dimensions may interfere with each other. To deal with this intrinsic shortage, this paper presents a DE algorithm framework with fine evaluation strategy. In the process of search, solution is updated and evaluated dimension by dimension. In each dimension, the updated value will be accepted only if it can improve the solution. In case that there is no improvement found in any dimension, the new solution, which is calculated using classical mutation operator only, will be accepted in low probability. This strategy can improve diversification and keep DE algorithm from premature convergence. Simulation experiments were carried on typical benchmark functions, and the results show that fine evaluation strategy can improve the performance of DE algorithm remarkably.