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 :
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