An introduction to differential evolution
New ideas in optimization
Journal of Global Optimization
Group properties of crossover and mutation
Evolutionary Computation
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Structural Search Spaces and Genetic Operators
Evolutionary Computation
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 Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Differential evolution (DE) algorithm has a wide use in optimization problems, whose performance is closely related to the separability of the fitness function. In this paper, we propose Principle Coordinate (PC) strategy, a new adaptive control strategy to improve DE's performance. PC attempts to maximize the fitness function's separability and make crossover operator more robust through coordinate rotation. In PC, Principal Component Analysis (PCA) is adopted to draw the ideal coordinate system from the difference vectors distribution. In the numerical experiments, PC is combined with two versions of classical DE algorithms to test its ability. The first experiment measures the accuracy of the coordinate system obtained by PC. In the second experiment, four benchmark functions and an engineering project are used to evaluate PC's efficiency. The results show that PC improves DE's efficiency, robustness and stability.