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Soft Computing
Published in: Soft Computing 12/2017

18-01-2016 | Methodologies and Application

Democracy-inspired particle swarm optimizer with the concept of peer groups

Authors: Ritambhar Burman, Soumyadeep Chakrabarti, Swagatam Das

Published in: Soft Computing | Issue 12/2017

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Abstract

This article proposes to integrate the concept of governance in human society with the nature-inspired particle swarm optimization (PSO) algorithm. A population-based iterative global optimization algorithm, called Democracy-inspired particle swarm optimization with the concept of peer groups (DPG-PSO) has been developed for solving multidimensional, non-linear, non-convex, and multimodal optimization problems by exploiting the concept of the new peer-influenced topology. Here the particles, each of which model a candidate solution of the problem under consideration, are given a choice to follow two possible leaders who have been selected on the basis of a voting mechanism. The leader and the opposition have their influences proportional to the total number of votes polled in their favor. A detailed empirical study comprising tuning of DPG-PSO parameters and its optimizing mechanism has been presented in the paper. The algorithm is tested in a standard benchmark suite consisting of unimodal, multimodal, shifted and rotated functions. DPG-PSO is found to statistically outperform seven other well-known PSO variants in terms of final accuracy and robustness over majority of the test cases, thus, proving itself as an efficient algorithm.
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Metadata
Title
Democracy-inspired particle swarm optimizer with the concept of peer groups
Authors
Ritambhar Burman
Soumyadeep Chakrabarti
Swagatam Das
Publication date
18-01-2016
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 12/2017
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-2007-8

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