Name
Abstract | ||
|---|---|---|
Differential Evolution (DE) is a highly competitive and powerful real parameter optimizer in the diverse community of evolutionary algorithms. The performance of DE depends largely upon its control parameters and is quite sensitive to their appropriate settings. One of those parameters commonly known as scale factor or F, controls the step size of the vector differentials during the search. During the exploration stage of the search, large step sizes may prove more conducive while during the exploitation stage, smaller step sizes might become favorable. This work proposes a simple and effective technique that alters F in stages, first through random perturbations and then through the application of an annealing schedule. We report the performance of the new variant on 20 benchmark functions of varying complexity and compare it with the classic DE algorithm (DE/Rand/1/bin), two other scale factor altering variants, and state of the art, SaDE. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SDE.2014.7031528 | SDE |
Keywords | Field | DocType |
optimisation,sade,evolutionary computation,benchmark functions,search exploration stage,parameter optimizer,vector differentials,evolutionary algorithms,dither,classic de algorithm,annealing schedule,differential evolution,annealed scale factor,exploitation stage,vectors,random perturbations,benchmark testing,sociology,annealing,statistics | Scale factor,Differential (mechanical device),Mathematical optimization,Evolutionary algorithm,Bin,Evolutionary computation,Differential evolution,Dither,Engineering,Benchmark (computing) | Conference |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deepak Dawar | 1 | 5 | 1.75 |
| Simone A Ludwig | 2 | 1309 | 179.41 |