Proceedings Article10.1109/CEC.2006.1688311
Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization
Janez Brest,Viljem Zumer,Mirjam Sepesy Maučec +2 more
- 11 Sep 2006
- pp 215-222
TL;DR: The performance of the self-adaptive differential evolution algorithm is evaluated on the set of 24 benchmark functions provided for the CEC2006 special session on constrained real parameter optimization.
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Abstract: Differential Evolution (DE) has been shown to be a powerful evolutionary algorithm for global optimization in many real problems. Self-adaptation has been found to be high beneficial for adjusting control parameters during evolutionary process, especially when done without any user interaction. In this paper we investigate a self-adaptive differential evolution algorithm where more DE strategies are used and control parameters F and CR are self-adapted. The performance of the self-adaptive differential evolution algorithm is evaluated on the set of 24 benchmark functions provided for the CEC2006 special session on constrained real parameter optimization.
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Citations
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Population size reduction for the differential evolution algorithm
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TL;DR: The original version uses fixed population size but a method for gradually reducing population size is proposed, which improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm.
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References
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Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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Kenneth Price,Rainer Storn,Jouni Lampinen +2 more
- 13 Dec 2005
TL;DR: This volume explores the differential evolution (DE) algorithm in both principle and practice and is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
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•Book
Differential Evolution: A Practical Approach to Global Optimization
Kenneth Price,Rainer Storn,Jouni Lampinen +2 more
- 25 Nov 2014
TL;DR: The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast as discussed by the authors, which is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimisation.
5.6K
Introduction to Evolutionary Computing
Agoston E. Eiben,James C. Smith +1 more
- 01 Jan 2015
TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.
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Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces
Kenneth Price
- 01 Jan 2004
TL;DR: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented and it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach.
4.3K