Journal Article10.1109/TCYB.2015.2399478
Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies
Qinqin Fan,Xuefeng Yan +1 more
190
TL;DR: In the proposed algorithm, the mutation strategies are automatically adjusted with population evolution, and the control parameters evolve in their own zoning to self-adapt and discover near optimal values autonomously.
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Abstract: The performance of the differential evolution (DE) algorithm is significantly affected by the choice of mutation strategies and control parameters. Maintaining the search capability of various control parameter combinations throughout the entire evolution process is also a key issue. A self-adaptive DE algorithm with zoning evolution of control parameters and adaptive mutation strategies is proposed in this paper. In the proposed algorithm, the mutation strategies are automatically adjusted with population evolution, and the control parameters evolve in their own zoning to self-adapt and discover near optimal values autonomously. The proposed algorithm is compared with five state-of-the-art DE algorithm variants according to a set of benchmark test functions. Furthermore, seven nonparametric statistical tests are implemented to analyze the experimental results. The results indicate that the overall performance of the proposed algorithm is better than those of the five existing improved algorithms.
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Citations
Ensemble of differential evolution variants
Guohua Wu,Guohua Wu,Xin Shen,Haifeng Li,Huangke Chen,Anping Lin,Ponnuthurai Nagaratnam Suganthan +6 more
TL;DR: The success of EDEV reveals that, through an appropriate ensemble framework, different DE variants of different merits can support one another to cooperatively solve optimization problems.
379
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.
253
Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
TL;DR: This study describes in depth the structural analysis and working principle that underlie the promising and recent work in this field, to analyze their advantages and disadvantages and to gain future insights that can further improve these algorithms.
204
Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization
TL;DR: A novel segment-based predominant learning swarm optimizer (SPLSO) Swarm optimizer through letting several predominant particles guide the learning of a particle is proposed, which evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities.
158
Finding Multiple Roots of Nonlinear Equation Systems via a Repulsion-Based Adaptive Differential Evolution
TL;DR: A repulsion-based adaptive DE, called RADE, is proposed for finding multiple roots of NESs in a single run to enhance the search ability and remedy the trial-and-error tuning of the parameters of differential evolution (DE) for different problems.
References
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.
Individual Comparisons by Ranking Methods
TL;DR: The comparison of two treatments generally falls into one of the following two categories: (a) a number of replications for each of the two treatments, which are unpaired, or (b) we may have a series of paired comparisons, some of which may be positive and some negative as mentioned in this paper.
14.5K
Use of Ranks in One-Criterion Variance Analysis
William Kruskal,W. Allen Wallis +1 more
TL;DR: In this article, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C − 1), unless the samples were too small, in which case special approximations or exact tables are provided.
11.4K
•Book
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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