Paper Info
Title
Vine creeping algorithm for global optimisation
Abstract
This paper presents a novel vine creeping optimisation algorithm based on the integration of the Levenberg-Marquardt algorithm into a revised non-revisiting genetic algorithm. The global search of the genetic algorithm is enhanced in efficiency and accuracy by incorporating the Levenberg-Marquardt algorithm into the selection and mutation process. The term revisit is redefined as a local region of convergence by the Levenberg-Marquardt algorithm, rather than a particular point selected. The redefinition of a revisit allows a larger step size in mutation hence reducing the number of evaluations in order to flag the current space as saturated. The effect of the revisited regions filling out the current local minimum regions and branching into unvisited space results in the vine creeping effect. The proposed algorithm was tested against three well known benchmark functions, and was able to converge upon the global optimum within an average of 63.91 generations, with a success rate ranging between 96-100%.
Year
DOI
Venue
2010
10.1109/NABIC.2010.5716334
Nature and Biologically Inspired Computing
Keywords
Field
DocType
genetic algorithms,search problems,Levenberg-Marquardt algorithm,global optimisation,global search,mutation process,revised nonrevisiting genetic algorithm,selection process,vine creeping algorithm,Binary Space Partition,Evolutionary Algorithms,Genetic Algorithm,Global Optimisation,Levenberg-Marquardt
Convergence (routing),Search algorithm,Evolutionary algorithm,FSA-Red Algorithm,Artificial intelligence,Population-based incremental learning,Genetic algorithm,Mathematical optimization,Algorithm,Cultural algorithm,Machine learning,Mathematics,Levenberg–Marquardt algorithm
Conference
ISBN
Citations 
PageRank 
978-1-4244-7377-9
0
0.34
References 
Authors
4
3
Name
Order
Citations
PageRank
Christopher N. Young100.34
Ju Jia Zou219820.00
Chin Jian Leo300.34