A new differential mutation base generator for differential evolution
Journal of Global OptimizationPublished 26 February 2010
Chuan Lin, Anyong Qing, Quanyuan Feng
Citations23
SJR quartileQ1
SJR score0.81
SNIP1.46
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Numerical results show that a better balance of reliability and efficiency can be obtained in differential evolution implementing the new generator of differential mutation base, especially in functions with high dimension.
Abstract
Differential evolution, Differential mutation, Best of random, Diversity,
Keywords
Computer Science
Journal of Global OptimizationDifferential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
28,706 Citations1997Rainer Storn, Kenneth V. Price
It is demonstrated that the new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous spacefunctions Converges faster and with more certainty than manyother acclaimed global optimization methods.
Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces
3,354 Citations2004Kenneth V. Price
A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented and it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach.
A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems
1,205 Citations2005Jørgen Vesterstrøm, René Thomsen
The results from this study show that DE generally outperforms the other algorithms, however, on two noisy functions, both DE and PSO were outperformed by the EA.
On the usage of differential evolution for function optimization
1,088 Citations2002Rainer Storn
This paper describes several variants of DE and elaborates on the choice of DE's control parameters, which corresponds to the application of fuzzy rules, and the design of a howling removal unit with DE.
Differential Evolution: In Search of Solutions
372 Citations2006Vitaliy Feoktistov
Computational Statistics & Data AnalysisDifferential evolution and particle swarm optimisation in partitional clustering
294 Citations2005Sandra Paterlini, Thiemo Krink
The empirical results show that DE is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both with respect to precision as well as robustness (reproducibility) of the results.
IEEE Transactions on Geoscience and Remote SensingDynamic differential evolution strategy and applications in electromagnetic inverse scattering problems
191 Citations2005Anyong Qing
It has been observed that the dynamic differential evolution strategy significantly outperforms the differential Evolution strategy in efficiency, robustness, and memory requirement.
IEEE Transactions on Antennas and PropagationElectromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy
170 Citations2003Anyong Qing
CERN Document Server (European Organization for Nuclear Research)Differential Evolution: Fundamentals and Applications in Electrical Engineering
112 Citations2009Anyong Qing
Differential Evolution, an Alternative Approach to Evolutionary Algorithm
96 Citations2007Kit Po Wong, Zhaoyang Dong
A study on base vector for differential evolution
25 Citations2008Anyong Qing
A comprehensive comparative parametric study on differential evolution shows that strategies applying random base vector are neither efficient nor robust.
A parametric study on differential evolution based on benchmark electromagnetic inverse scattering problem
16 Citations2007Anyong Qing
The findings presented here should be highly beneficial to future applicants of differential evolution in the community of electromagnetic inverse scattering problems and may be directly or indirectly applicable to researchers in other areas.
Multisensor fusion and model selection using a minimal representation size framework
10 Citations2002R. Joshi, Arthur C. Sanderson
The minimal representation size (MRS) criterion is used as a basis for the selection of a minimal complexity model among a class of stored models, which results in a "universal yardstick" for model selection which is easily adapted to new combinations of sensors and parameters.