Discrete optimization problem solving with three variants of hybrid binary particle swarm optimization

Authors:
Vikas Singh;Deepak Singh;Ritu Tiwari;Anupam Shukla
Affiliations:
ABV- Indian Institute of Information Technology and management, Gwalior, Gwalior, India;Raipur Institute of Technology, Raipur, India;ABV- Indian Institute of Information Technology and management, Gwalior, Gwalior, India;ABV- Indian Institute of Information Technology and management, Gwalior, Gwalior, India
Venue:
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Year:
2011

Citing 4
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A Study of Global Optimization Using Particle Swarms

Journal of Global Optimization
Self-Adaptive Crossover Particle Swarm Optimizer for Multi-dimension Functions Optimization

ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
The Analysis and Improvement of Binary Particle Swarm Optimization

CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01

Quantified Score

Hi-index	0.00

Visualization

Abstract

Binary Particle Swarm Optimization (BPSO) is a population based stochastic algorithm for discrete optimization inspired by social behavior of bird flocking or fish schooling that has been successfully applied in different areas. However, its potential has not been sufficiently explored. Recent works have proposed hybridization of BPSO with promising results. This paper aims to present three variants of hybrid BPSO algorithm, which is differently to the previous approaches. This work, maintains the main BPSO concept for the update of the velocity of the particle and position, one additional step is added to the method that is crossover technique of Genetic Algorithm. The paper describes the three proposed algorithms and a set of experiments with the standard benchmark functions. The hybrid algorithm shows competitive results compared to Classical BPSO.