Differential Evolution Based on Improved Learning Strategy

Authors:
Yuan Shi;Zhen-Zhong Lan;Xiang-Hu Feng
Affiliations:
Software School, Sun Yat-sen University, Guangzhou, P.R. China;Software School, Sun Yat-sen University, Guangzhou, P.R. China;Software School, Sun Yat-sen University, Guangzhou, P.R. China
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

Citing 3
Cited 0

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

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Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation

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Abstract

From a learning perspective, the mutation scheme in differential evolution (DE) can be regarded as a learning strategy. When mutating, three random individuals are selected and placed in a random order. This strategy, however, probably suffers some drawbacks which can slow down the convergence rate. To improve the efficiency of classic DE, this paper proposes a differential evolution based on improved learning strategy (ILSDE). The proposed learning strategy, inspired by the learning theory of Confucius, places the three individuals in a more reasonable order. Experimenting with 23 test functions, we demonstrate that ILSDE performs better than classic DE.