Hybrid Particle Swarm Optimizer with Mutation

Authors:
Ahmed Ali Abdala Esmin;Germano Lambert-Torres
Affiliations:
Federal University of Itajubá;Federal University of Itajubá
Venue:
Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005
Year:
2005

Citing 6
Cited 0

Optimization of control parameters for genetic algorithms

IEEE Transactions on Systems, Man and Cybernetics
A computer-aided process planning model based on genetic algorithms

Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a Hybrid Particle Swarm Optimizers combining the idea of the particle swarm with concepts from Evolutionary Algorithms. The hybrid Particle Swarm Optimizers with Mutation (HPSOM) combine the traditional velocity and position update rules with the idea of numerical mutation. This model is tested and compared with the standard PSO on unimodal and multimodal functions. This is done to illustrate that PSOs with mutation operation have the potential to achieve faster convergence and the potential to find a better solution. The objective of this paper is to describe the HPSOM model and to test their potential and competetiveness on function optimization.