Title

Generation Of Optimal Functions Using Particle Swarm Method Over Discrete Intervals

Abstract

Particle swarm optimization is a computational learning technique designed to find a global and optimal solution upon or within a function. The output, usually singular, is characteristically accurate as the nature of the system is to maintain a balance of convergence and sample diversity. This paper aims to introduce the process of using a multi-level evaluation approach of particle swarm optimization to generate a solution function. Multiple variable assessment is replaced with sequential interval assessment of repeated variables and pieced together to form the framework of an optimized function. ©2009 IEEE.

Publication Date

11-2-2009

Publication Title

Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/NAFIPS.2009.5156484

Socpus ID

70350402296 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/70350402296

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