Submit Manuscript

Information Technology Journal

Year: 2011 | Volume: 10 | Issue: 8 | Page No.: 1536-1544
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Article Trend

Total views 67

Authors

Hui Lu

Xiao Chen

Keywords

Research Article

A New Particle Swarm Optimization with a Dynamic Inertia Weight for Solving Constrained Optimization Problems

Hui Lu and Xiao Chen
This study has presented an enhanced particle swarm optimization approach which is designed to solve constrained optimization problems. The approach incorporates a dynamic inertia weight in order to help the algorithm to find the global and overcome the problem of premature convergence to local optima. The inertia weight of every individual is dynamically controlled by the Euclidean distance between individual and global best individual. The approach was tested with a well-known benchmark. Simulation results show that the suitability of the proposed algorithm in terms of effectiveness and robustness.
PDF Fulltext XML References Citation

Related Articles

Efficient Scheduling of Electricity Consumption for Smart Grid with Uncertainty in Renewable Supply
A Layout Pattern Based Particle Swarm Optimization for Constrained Packing Problems
Performance of Gain Scheduled Generic Model Controller Based on BF-PSO for a Batch Reactor
Multi-objective Optimization using Chaos Based PSO
An Asynchronous Parallel Particle Swarm Optimization Algorithm for a Scheduling Problem
Migration Algorithm of Particle Swarm Optimization For a Scheduling Problem
Optimal Design of Load Shedding and Generation Reallocation in Power Systems Using Fuzzy Particle Swarm Optimization Algorithm
Short-Term Prediction of Traffic Rate Interval Router Using Hybrid Training of Dynamic Synapse Neural Network Structure
Research on Structure Learning of Product Unit Neural Networks by Particle Swarm Optimization

Leave a Comment

Your email address will not be published. Required fields are marked *