By Topic

Adaptive Particle Swarm Optimization Algorithm in Dynamic Environments

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Rezazadeh, I. ; Dept. of Comput. & Electr., Qazvin Islamic Azad Univ., Qazvin, Iran ; Meybodi, M.R. ; Naebi, A.

Many real world optimization problems are dynamic in which global optimum and local optimum change over time. Particle swarm optimization has performed well to find and track optimum in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm for increase of convergence speed has been adaptive configured by inertia weight value and to improve obtained solutions uses from a local search and to avoid wasting function evaluation of stopped swarms. To improve the search performance, when the search areas of two swarms are overlapped, the worse swarms will be removed. Moreover, in order to track quickly the changes in the environment, all particles in the swarm convert to quantum particles when a change in the environment is detected. Experimental results on different dynamic environments modeled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, for all evaluated environments.

Published in:

Computational Intelligence, Modelling and Simulation (CIMSiM), 2011 Third International Conference on

Date of Conference:

20-22 Sept. 2011