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research-article

JADE: adaptive differential evolution with optional external archive

Authors:

Jingqiao Zhang,

Arthur C. SandersonAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 13, Issue 5

Pages 945 - 958

https://doi.org/10.1109/TEVC.2009.2014613

Published: 01 October 2009 Publication History

Abstract

A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy "DE/current-to-pbest" with optional external archive and updating control parameters in an adaptive manner. The DE/current-to- pbest is a generalization of the classic "DE/current-to-best," while the optional archive operation utilizes historical data to provide information of progress direction. Both operations diversify the population and improve the convergence performance. The parameter adaptation automatically updates the control parameters to appropriate values and avoids a user's prior knowledge of the relationship between the parameter settings and the characteristics of optimization problems. It is thus helpful to improve the robustness of the algorithm. Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems. JADE with an external archive shows promising results for relatively high dimensional problems. In addition, it clearly shows that there is no fixed control parameter setting suitable for various problems or even at different optimization stages of a single problem.

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Published In

cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 13, Issue 5

October 2009

249 pages

ISSN:1089-778X

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Copyright © 2009.

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IEEE Press

Publication History

Published: 01 October 2009

Accepted: 08 May 2008

Revised: 27 January 2008

Received: 12 November 2007

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Hu XNing BWu JGu QCai B(2024)A Fitness Distance Correlation-Based Adaptive Differential Evolution for Nonlinear Equations SystemsInternational Journal of Swarm Intelligence Research10.4018/IJSIR.35206015:1(1-22)Online publication date: 17-Sep-2024
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Zhou TZhang WNiu BHe PYue G(2024)Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement LearningInternational Journal of Intelligent Systems10.1155/2024/67407012024Online publication date: 1-Jan-2024
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