Journal ArticleDOI
Differential evolution with improved individual-based parameter setting and selection strategy
Mengnan Tian,Xingbao Gao,Cai Dai +2 more
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TLDR
A novel differential evolution algorithm is proposed to improve the search efficiency of DE by employing the information of individuals to adaptively set the parameters of DE and update population.About:
This article is published in Applied Soft Computing.The article was published on 2017-07-01. It has received 41 citations till now. The article focuses on the topics: Mutation (genetic algorithm) & Differential evolution.read more
Citations
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Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem
TL;DR: The proposed WMSDE can avoid premature convergence, balance local search ability and global search ability, accelerate convergence, improve the population diversity and the search quality, and is compared with five state-of-the-art DE variants by 11 benchmark functions.
Journal ArticleDOI
An adaptive differential evolution with combined strategy for global numerical optimization
TL;DR: This paper proposes a novel DE variant by introducing a series of combined strategies into DE, called CSDE, which achieves the best overall performance among the eight DE variants.
Journal ArticleDOI
CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems
TL;DR: CCSA is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm and finds the best optimal solution for the applied problems of engineering design.
Journal ArticleDOI
Differential evolution: A recent review based on state-of-the-art works
TL;DR: This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years.
Journal ArticleDOI
Differential evolution: A recent review based on state-of-the-art works
TL;DR: Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997 as discussed by the authors .
References
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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.
Journal ArticleDOI
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
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.
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.
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
JADE: Adaptive Differential Evolution With Optional External Archive
Jingqiao Zhang,A.C. Sanderson +1 more