DocumentCode
1874713
Title
Kudu herd optimization
Author
Boelaert, Julien
Author_Institution
CES, Univ. Paris 1 Pantheon-Sorbonne, Paris, France
fYear
2012
fDate
6-8 Sept. 2012
Firstpage
108
Lastpage
113
Abstract
This work proposes a new and simple algorithm for unconstrained numeric optimization over continuous spaces. A population of candidate solutions styled as a herd of kudus performs a succession of jumps through the search space in order to find the best solution (the kudu is a species of antelope). The logic of this algorithm is quite different from that of most population-based algorithms, as the individual solutions are moved together in a herd-like fashion. Performance comparisons are conducted with the Artificial Bee Colony, Differential Evolution, the Genetic Algorithm and Particle Swarm Optimization on benchmark functions. The kudu herd seems to perform well in the early stages and on high-dimensional problems.
Keywords
genetic algorithms; particle swarm optimisation; search problems; artificial bee colony; candidate solutions; continuous spaces; differential evolution; genetic algorithm; high-dimensional problems; kudu herd optimization; particle swarm optimization; population-based algorithms; search space; unconstrained numeric optimization; Lead; Optimization; Particle swarm optimization; Sociology; Standards; Statistics; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems (IS), 2012 6th IEEE International Conference
Conference_Location
Sofia
Print_ISBN
978-1-4673-2276-8
Type
conf
DOI
10.1109/IS.2012.6335199
Filename
6335199
Link To Document