Paper Info
Title
Self-adaptive Hybrid differential evolution with simulated annealing algorithm for numerical optimization
Abstract
A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience. The performance of the SaDESA is evaluated on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization. Comparative study exposes the SaDESA algorithm as a competitive algorithm for a global optimization.
Year
DOI
Venue
2008
10.1109/CEC.2008.4630947
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
self-adjusting systems,numerical optimization,simulated annealing algorithm,learning systems,global optimization,real parameter optimization,self-adaptive hybrid differential evolution,learning strategy,simulated annealing,sadesa algorithm,mathematics,algorithm design and analysis,robust control,stochastic processes,encoding,differential evolution,comparative study,computer science,accuracy,optimization,computational modeling,benchmark testing,chromium
Simulated annealing,Mathematical optimization,Algorithm design,Global optimization,Computer science,Meta-optimization,Differential evolution,Adaptive simulated annealing,Self adaptive,Artificial intelligence,Machine learning,Benchmark (computing)
Conference
ISBN
Citations 
PageRank 
978-1-4244-1823-7
9
0.58
References 
Authors
7
4
Name
Order
Citations
PageRank
Zhongbo Hu1667.43
Qinghua Su2455.74
Sheng-wu Xiong3132.16
Fu-gao Hu490.58