Proceedings ArticleDOI
Dynamic optimization using Self-Adaptive Differential Evolution
Janez Brest,Aleš Zamuda,Borko Boskovic,Mirjam Sepesy Maučec,Viljem Zumer +4 more
- pp 415-422
TLDR
A Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used.Abstract:
In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used. The performance of the jDE algorithm is evaluated on the set of benchmark functions provided for the CEC 2009 special session on evolutionary computation in dynamic and uncertain environments.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
Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm
TL;DR: The statistical tests indicate that the problem-solving success of DS algorithm in transforming the geocentric cartesian coordinates into geodetic coordinates is higher than those of all classical methods and Computational-Intelligence algorithms used in this paper.
Journal ArticleDOI
Self-adaptive differential evolution algorithm using population size reduction and three strategies
Janez Brest,Mirjam Sepesy Maučec +1 more
TL;DR: The results show that the jDElscop algorithm can deal with large-scale continuous optimization effectively and behaves significantly better than other three algorithms used in the comparison, in most cases.
Journal ArticleDOI
Ensemble strategies for population-based optimization algorithms – a survey
TL;DR: A survey on the use of ensemble strategies in POAs is provided and an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. are provided and compare them with the ensemble Strategies in the context of POAs.
Book
Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence
TL;DR: This paper presents a meta-anatomy of evolutionary algorithms and some examples of successful and unsuccessful attempts at optimization in the context of discrete-time programming.
References
More filters
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.
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.
Journal ArticleDOI
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
Journal ArticleDOI
Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems
TL;DR: The results show that the algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained.
Related Papers (5)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more