A memory binary particle swarm optimization

Zhen Ji; Tao Tian; Shan He; Zexuan Zhu

doi:10.1109/CEC.2012.6256150

A memory binary particle swarm optimization

Zhen Ji^*, Tao Tian, Shan He, Zexuan Zhu

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

This paper proposes a memory binary particle swarm optimization algorithm (MBPSO) based on a new updating strategy. Unlike the traditional binary PSO, which updates the binary bits of a particle ignoring their previous status, MBPSO memorizes the bit status and updates them according to a new defined velocity. As such, precious historical information could be retained to guide the search. The velocity vector of MBPSO is designed as a probability for deciding whether the particle bits change or not. The proposed algorithm is tested on four discrete benchmark functions. The experimental results reported over 100 runs show that MBPSO is capable of obtaining encouraging performance in discrete optimization problems.

Original language	English
Title of host publication	2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs	https://doi.org/10.1109/CEC.2012.6256150
Publication status	Published - 2012
Event	2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia Duration: 10 Jun 2012 → 15 Jun 2012

Publication series

Name	2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference	2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	10/06/12 → 15/06/12

Bibliographical note

Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.

Keywords

Binary Particle Swarm Optimization
Discrete PSO
PSO

ASJC Scopus subject areas

Computational Theory and Mathematics
Theoretical Computer Science

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10.1109/CEC.2012.6256150

Cite this

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title = "A memory binary particle swarm optimization",

abstract = "This paper proposes a memory binary particle swarm optimization algorithm (MBPSO) based on a new updating strategy. Unlike the traditional binary PSO, which updates the binary bits of a particle ignoring their previous status, MBPSO memorizes the bit status and updates them according to a new defined velocity. As such, precious historical information could be retained to guide the search. The velocity vector of MBPSO is designed as a probability for deciding whether the particle bits change or not. The proposed algorithm is tested on four discrete benchmark functions. The experimental results reported over 100 runs show that MBPSO is capable of obtaining encouraging performance in discrete optimization problems.",

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language = "English",

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AU - Ji, Zhen

AU - Tian, Tao

AU - He, Shan

AU - Zhu, Zexuan

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AB - This paper proposes a memory binary particle swarm optimization algorithm (MBPSO) based on a new updating strategy. Unlike the traditional binary PSO, which updates the binary bits of a particle ignoring their previous status, MBPSO memorizes the bit status and updates them according to a new defined velocity. As such, precious historical information could be retained to guide the search. The velocity vector of MBPSO is designed as a probability for deciding whether the particle bits change or not. The proposed algorithm is tested on four discrete benchmark functions. The experimental results reported over 100 runs show that MBPSO is capable of obtaining encouraging performance in discrete optimization problems.

KW - Binary Particle Swarm Optimization

KW - Discrete PSO

KW - PSO

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U2 - 10.1109/CEC.2012.6256150

DO - 10.1109/CEC.2012.6256150

M3 - Conference contribution

AN - SCOPUS:84866875022

SN - 9781467315098

T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

A memory binary particle swarm optimization

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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