Improving Particle Swarm Optimization with Differentially Perturbed Velocity

Swagatam Das; Amit Konar; Uday Kumar Chakraborty

doi:10.1145/1068009.1068037

Improving Particle Swarm Optimization with Differentially Perturbed Velocity

Swagatam Das, Amit Konar, Uday Kumar Chakraborty

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Abstract

This paper introduces a novel scheme of improving the performance of particle swarm optimization (PSO) by a vector differential operator borrowed from differential evolution (DE). Performance comparisons of the proposed method are provided against (a) the original DE, (b) the canonical PSO, and (c) three recent, high-performance PSO-variants. The new algorithm is shown to be statistically significantly better on a seven-function test suite for the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.

Original language	American English
Journal	Genetic and Evolutionary Computation Conference
DOIs	https://doi.org/10.1145/1068009.1068037
State	Published - Jun 25 2005

Disciplines

Mathematics
Applied Mathematics
Artificial Intelligence and Robotics

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10.1145/1068009.1068037License: Unspecified

Cite this

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abstract = " This paper introduces a novel scheme of improving the performance of particle swarm optimization (PSO) by a vector differential operator borrowed from differential evolution (DE). Performance comparisons of the proposed method are provided against (a) the original DE, (b) the canonical PSO, and (c) three recent, high-performance PSO-variants. The new algorithm is shown to be statistically significantly better on a seven-function test suite for the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability. ",

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AB - This paper introduces a novel scheme of improving the performance of particle swarm optimization (PSO) by a vector differential operator borrowed from differential evolution (DE). Performance comparisons of the proposed method are provided against (a) the original DE, (b) the canonical PSO, and (c) three recent, high-performance PSO-variants. The new algorithm is shown to be statistically significantly better on a seven-function test suite for the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.

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Improving Particle Swarm Optimization with Differentially Perturbed Velocity

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