Multi-phase generalization of the particle swarm optimization algorithm

Buthainah Al-Kazemi; Chilukuri K. Mohan

doi:10.1109/CEC.2002.1006283

Multi-phase generalization of the particle swarm optimization algorithm

Buthainah Al-Kazemi, Chilukuri K. Mohan

Research output: Contribution to conference › Paper › peer-review

65 Scopus citations

Abstract

Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming.

Original language	English (US)
Pages	489-494
Number of pages	6
DOIs	https://doi.org/10.1109/CEC.2002.1006283
State	Published - 2002
Externally published	Yes
Event	2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002

Other

Other	2002 Congress on Evolutionary Computation, CEC 2002
Country/Territory	United States
City	Honolulu, HI
Period	5/12/02 → 5/17/02

ASJC Scopus subject areas

Software

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10.1109/CEC.2002.1006283

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title = "Multi-phase generalization of the particle swarm optimization algorithm",

abstract = "Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming.",

author = "Buthainah Al-Kazemi and Mohan, {Chilukuri K.}",

year = "2002",

doi = "10.1109/CEC.2002.1006283",

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AU - Al-Kazemi, Buthainah

AU - Mohan, Chilukuri K.

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AB - Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming.

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M3 - Paper

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T2 - 2002 Congress on Evolutionary Computation, CEC 2002

Y2 - 12 May 2002 through 17 May 2002

ER -

Multi-phase generalization of the particle swarm optimization algorithm

Abstract

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