Tae Jong Choi
ABSTRACT
A new variant of Differential Evolution (DE) algorithm called A-SaDE algorithm is proposed. A-SaDE algorithm is based on two DE variants, Self-adaptive DE (SaDE) and Asynchronous DE (ADE) algorithms. SaDE algorithm is one of the well-known DE algorithms and shows powerful optimization performance by automatically tuning the mutation strategies as well as the control parameters. The asynchronous scheme is recently proposed, and it helps to find better solutions by increasing the greediness. We incorporated these two algorithms called A-SaDE algorithm and tested it with 13 scalable benchmark problems. The experimental results confirm that A-SaDE algorithm outperforms original SaDE algorithm, especially for unimodal functions.
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Digital Library
- Choi, T. J., & Ahn, C. W. (2017, February). An Improved Differential Evolution Algorithm and Its Application to Large-Scale Artificial Neural Networks. In Journal of Physics: Conference Series (Vol. 806, No. 1, p. 012010). IOP Publishing.Google Scholar
- Choi, T. J., & Ahn, C. W. (2017, December). Adaptive Cauchy Differential Evolution with Strategy Adaptation and Its Application to Training Large-Scale Artificial Neural Networks. In International Conference on Bio-Inspired Computing: Theories and Applications (pp. 502--510). Springer, Singapore.Google ScholarCross Ref
- Choi, T. J., & Ahn, C. W. (2017). Artificial life based on boids model and evolutionary chaotic neural networks for creating artworks. Swarm and Evolutionary Computation.Google Scholar
- Choi, T. J., Ahn, C. W., & An, J. (2013). An adaptive cauchy differential evolution algorithm for global numerical optimization. The Scientific World Journal, 2013.Google ScholarCross Ref
- Choi, T. J., & Ahn, C. W. (2014). An adaptive differential evolution algorithm with automatic population resizing for global numerical optimization. In Bio-Inspired Computing-Theories and Applications (pp. 68--72). Springer, Berlin, Heidelberg.Google ScholarCross Ref
- Choi, T. J., & Ahn, C. W. (2015). An adaptive population resizing scheme for differential evolution in numerical optimization. Journal of Computational and Theoretical Nanoscience, 12(7), 1336--1350.Google ScholarCross Ref
- Choi, T. J., & Ahn, C. W. (2015). An adaptive cauchy differential evolution algorithm with population size reduction and modified multiple mutation strategies. In Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems-Volume 2 (pp. 13--26). Springer, Cham.Google ScholarCross Ref
- Choi, T. J., & Ahn, C. W. (2017). Adaptive α-stable differential evolution in numerical optimization. Natural Computing, 16(4), 637--657. Google ScholarDigital Library
- Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE transactions on Evolutionary Computation, 13(2), 398--417. Google ScholarDigital Library
- Choi, T. J., & Ahn, C. W. (2014). An Adaptive Cauchy Differential Evolution Algorithm with Bias Strategy Adaptation Mechanism for Global Numerical Optimization. JCP, 9(9), 2139--2145.Google Scholar
- Zhabitskaya, E., & Zhabitsky, M. (2012). Asynchronous differential evolution. In Mathematical Modeling and Computational Science (pp. 328--333). Springer, Berlin, Heidelberg. Google ScholarDigital Library
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Index Terms
- Asynchronous Differential Evolution with Strategy Adaptation for Global Numerical Optimization
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