Improving particle swarm optimization with differentially perturbed velocity

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. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Swagatam Das, Amit Konar, Uday Kumar Chakraborty
• Keywords: Canonical Pso, GECCO 2005, Particle Swarm Optimization, Seven-function Test Suite