A modified particle swarm optimizer using an adaptive dynamic weight scheme

Authors:
Shu-Kai S. Fan;Ju-Ming Chang
Affiliations:
Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan County, Taiwan, Republic of China;Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan County, Taiwan, Republic of China
Venue:
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Year:
2007

Citing 4
Cited 3

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
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization

Computers and Operations Research

Modified Particle Swarm Optimizer with Adaptive Dynamic Weights for Cancer Combinational Chemotherapy

ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
A note on the learning automata based algorithms for adaptive parameter selection in PSO

Applied Soft Computing
Effectiveness of particle swarm optimization technique in dealing with noisy data in inverse heat conduction analysis

SCSC '09 Proceedings of the 2009 Summer Computer Simulation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

Particle swarm optimization (PSO) is a stochastic, population-based optimization technique that is inspired by the emigrant behavior of a flock of birds searching for food. In this paper, a nonlinear function of decreasing inertia weight that adapts to current performance of PSO search is presented. Meanwhile, a dynamic mechanism to adjust decrease rates is also suggested. Through the experimental study, the new PSO algorithm with adaptive dynamic weight scheme is compared to the exiting models in terms of various benchmark functions. The computational experience shows some great promise.