Self-adaptive Differential Evolution
Lecture notes in computer sciencePublished 1 January 2005
Mahamed G. H. Omran, Ayed Salman, Andries P. Engelbrecht
Citations265
SJR quartileQ2
SJR score0.35
SNIP0.55
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
A self-adaptive DE (SDE) is proposed where parameter tuning is not required and the performance of SDE is investigated and compared with other versions of DE.
Abstract
Differential Evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a self-adaptive DE (SDE) is proposed where parameter tuning is not required. The performance of SDE is investigated and compared with other versions of DE. The experiments conducted show that SDE outperformed the other DE versions in all the benchmark functions.
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.
Mathematics and Computers in SimulationSimulated annealing: theory and applications.
3,005 Citations1988
Simulated Annealing: Theory and Applications
2,372 Citations1987Peter J. M. van Laarhoven, Emile Aarts
Performance of the simulated annealing algorithm and the relation with statistical physics and asymptotic convergence results are presented.
How to Solve It: Modern Heuristics
1,633 Citations2004Zbigniew Michalewicz, David B. Fogel
This book discusses computational experiments with heuristic methods for solving practical problems and basic concepts of probability and Statistics, as well as an Evolutionary approach to decision-making.
The self-adaptive Pareto differential evolution algorithm
401 Citations2003Hussein A. Abbass
The emphasis of this paper is to analyze the dynamics and behavior of SPDE, a new version of PDE with self-adaptive crossover and mutation that is very competitive with other EMO algorithms.
DergiPark (Istanbul University)A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm
351 Citations2004Derviş Karaboğa
From the simulation results, it was observed that the convergence speed of DE is significantly better than genetic algorithms, and seems to be a promising approach for engineering optimization problems.
Differential evolution for multi-objective optimizatior
233 Citations2004B. V. Babu, M.M.L. Jehan
Two test problems on multiobjective optimization (one simple general problem and the second one on an engineering application of cantilever design problem) are solved using differential evolution (DE), which is an improved version of genetic algorithm.
Generalization of the strategies in differential evolution
131 Citations2004Vitaliy Feoktistov, Stefan Janaqi
A generalization of the differential evolution's strategies is introduced by dividing them into four groups according to their differentiation principle, which leads to the new universal formula of differentiation.
Noisy optimization problems - a particular challenge for differential evolution?
128 Citations2004Thiemo Krink, Bogdan Filipič +1 more
It is demonstrated that standard methods of evolutionary optimization are able to outperform differential evolution on noisy problems when the fitness of candidate solutions approaches the fitness variance caused by the noise.
Studies in computational intelligenceComputational Intelligence: An Introduction
113 Citations2022Arya Yaghoubzadeh-Bavandpour, Omid Bozorg‐Haddad +2 more
A fuzzy adaptive differential evolution algorithm
95 Citations2004Junhong Liu, Jouni Lampinen
Experimental results, provided by the proposed algorithm for a set of standard test functions, outperformed those of the standard differential evolution algorithm for optimization problems with higher dimensionality.
High performance clustering with differential evolution
75 Citations2005Sandra Paterlini, Thiemo Krink
This work reports results of a performance comparison between a GA, PSO and DE for a medoid evolution clustering approach and concludes that DE rather than GAs should be primarily considered for tackling partitional clustering problems with numerical optimization.
Swarm directions embedded in fast evolutionary programming
48 Citations2003Chengjian Wei, Zhenya He +2 more
In this paper, swarm directions are embedded in fast evolutionary programming and the swarm direction for an individual supplies its place to be mutated.