NDLI: Particle swarm optimization with adaptive linkage learning

Content Provider	IEEE Xplore Digital Library
Author	Devicharan, D. Mohan, C.K.
Copyright Year	2004
Description	Author affiliation: Dept. of Electr., Electron. & Comput. Sci.,, Syracuse Univ., NY, USA (Devicharan, D.; Mohan, C.K.)
Abstract	In many problems, the quality of solutions and computational effort required by optimization algorithms can be improved by exploiting knowledge found in the linkages or interrelations between problem dimensions or components. These linkages are sometimes known a priori from the nature of the itself; in other cases linkages can be learned by sampling the data space prior to the application of the optimization algorithm. This paper presents a new version of the particle swarm optimization algorithm (PSO) that utilizes linkages between components, performing more frequent simultaneous updates on subsets of particle position components that are strongly linked. Prior to application of this linkage-sensitive PSO algorithm, problem specific linkages can be learned by examining a randomly chosen collection of points in the search space to determine the correlations in fitness changes resulting from perturbations in pairs of components of particle positions. The resulting algorithm, adaptive-linkage PSO (ALiPSO) has performed significantly better than the classical PSO, in simulations conducted so far on several test problems.
Sponsorship	IEEE Neural Network Soc. Evolutionary Programming Soc. IEE
Starting Page	530
Ending Page	535
File Size	372075
Page Count	6
File Format	PDF
ISBN	0780385152
DOI	10.1109/CEC.2004.1330902
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Couplings Equations Sampling methods Testing Computational modeling Performance evaluation Birds Insects Multidimensional systems
Content Type	Text
Resource Type	Article

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