Journal ArticleDOI
JADE: Adaptive Differential Evolution With Optional External Archive
Jingqiao Zhang,A.C. Sanderson +1 more
Reads0
Chats0
TLDR
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.Abstract:
A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy ldquoDE/current-to-p bestrdquo with optional external archive and updating control parameters in an adaptive manner. The DE/current-to-pbest is a generalization of the classic ldquoDE/current-to-best,rdquo 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.read more
Citations
More filters
Journal ArticleDOI
Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Journal ArticleDOI
Recent advances in differential evolution – An updated survey
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
Journal ArticleDOI
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
TL;DR: A novel method, called composite DE (CoDE), has been proposed, which uses three trial vector generation strategies and three control parameter settings and randomly combines them to generate trial vectors.
Journal ArticleDOI
Differential evolution algorithm with ensemble of parameters and mutation strategies
TL;DR: The performance of EPSDE is evaluated on a set of bound-constrained problems and is compared with conventional DE and several state-of-the-art parameter adaptive DE variants.
Proceedings ArticleDOI
Improving the search performance of SHADE using linear population size reduction
Ryoji Tanabe,Alex Fukunaga +1 more
TL;DR: L-SHADE is proposed, which further extends SHADE with Linear Population Size Reduction (LPSR), which continually decreases the population size according to a linear function and is quite competitive with state-of-the-art evolutionary algorithms.
References
More filters
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
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.
Book
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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.
Introduction to Evolutionary Computing
Agoston E. Eiben,James C. Smith +1 more
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.
Book
Differential Evolution: A Practical Approach to Global Optimization
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.
Related Papers (5)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more