DocumentCode :
3318305
Title :
Varying dimensional particle swarm optimization
Author :
Yan, Yanjun ; Osadciw, Lisa Ann
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY
fYear :
2008
fDate :
21-23 Sept. 2008
Firstpage :
1
Lastpage :
8
Abstract :
A new algorithm, varying dimensional particle swarm optimization (VD-PSO), is proposed for entities with varying-dimensional components and each component assumes continuous-valued parameters. Such problems are distinct from current benchmark problems where the dimension of the particles is fixed. One well-studied application of VD-PSO is probability density estimation by Gaussian Mixture Models. A particle in VD-PSO includes a discrete number as the number of components and a set of real-valued component parameters. The number of components varies according to a random scheme, which dictates how many sets of components remain or expand. The component parameters are matched up supporting a linked update. Three other methods, the last two of which are also proposed by us as intuitive attempts to solve the varying dimensional problems, are compared with VD-PSO: 1. binary-headered PSO, 2. exhaustive PSO, 3. discrete-headered PSO. Simulations on known data with specified components show that VD-PSO provides a competitive density estimation to the exhaustive PSO, but spends the least wall-clock-time among all algorithms, almost 12% of the exhaustive PSO, while the binary-headered PSO or discrete-headered PSO do not achieve similar performance, because the binary-headered PSO is adversely affected by the dummy parameters, and the discrete-headered PSO makes too many dimension variations before the parameters are well tuned. The new VD-PSO algorithm is a viable and efficient solution to varying dimensional optimization problems.
Keywords :
Gaussian processes; estimation theory; particle swarm optimisation; probability; random processes; Gaussian mixture model; continuous-valued parameter; discrete number; probability density estimation; random scheme; real-valued component parameter; varying dimensional particle swarm optimization; Aircraft manufacture; Clustering algorithms; Collision mitigation; Cost function; Optimal scheduling; Particle swarm optimization; Proteins; Resource management; Transceivers; USA Councils; Distribution Estimation; Gaussian Mixture Model; Particle Swarm Optimization; Varying Dimension;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Swarm Intelligence Symposium, 2008. SIS 2008. IEEE
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-2704-8
Electronic_ISBN :
978-1-4244-2705-5
Type :
conf
DOI :
10.1109/SIS.2008.4668310
Filename :
4668310
Link To Document :
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