Paper Info
Title
SRaDE: an adaptive differential evolution based on stochastic ranking
Abstract
In this paper, we propose a methodology to improve the performance of the standard Differential Evolution (DE) in constraint optimization applications, in terms of accelerating its search speed, and improving the success rate. One critical mechanism embedded in the approach is applying Stochastic Ranking (SR) to rank the whole population of individuals with both objective value and constraint violation to be compared. The ranked population is then in a better shape to provide useful information e.g. direction to guide the search process. The strength of utilizing the directional information can be further controlled by a parameter - population partitioning factor, which is adjusted according to the evolution stage and generations. Because the adaptive adjustment of the parameter is predefined and does not need user input, the resulting algorithm is free of definition of this extra parameter and easier to implement. The performance of the proposed approach, which we call SRaDE (Stochastic Ranking based Adaptive Differential Evolution) is investigated and compared with standard DE. The experimental results show that SRDE significantly outperforms, or at least is comparable with standard DE in all the tested benchmark functions. We also conducted an experiment to compare SRaDE with SRDE - a variant of Stochastic Ranking based Differential Evolution without adaptive adjustment of the population partitioning factor. Experimental results show that SRaDE can also achieve improved performance over SRDE.
Year
DOI
Venue
2009
10.1145/1569901.1570209
GECCO
Keywords
Field
DocType
stochastic ranking,differential evolution,standard de,standard differential evolution,extra parameter,improved performance,adaptive differential evolution,adaptive adjustment,whole population,constraint optimization,constrained optimization
Population,Mathematical optimization,Ranking,Computer science,Differential evolution,Artificial intelligence,Constraint violation,Machine learning,Constrained optimization
Conference
Citations 
PageRank 
References 
2
0.39
4
Authors
3
Name
Order
Citations
PageRank
Jinchao Liu1242.71
Zhun Fan232435.30
Erik Goodman314515.19