By Topic

The convergence analysis and parameter selection of Artificial Physics Optimization algorithm

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
$33 $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)
Liping Xie ; Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, 030024, China ; Ying Tan ; Jianchao Zeng

Artificial Physics Optimization (APO) algorithm is a population-based stochastic algorithm based on Physicomimetics framework. The algorithm utilizes an attraction-repulsion mechanism to move individuals toward optimality. The convergence analysis of APO algorithm is made theoretically. By regarding each individual's position on each evolutionary step as a stochastic vector, APO algorithm determined by non-negative real parameter tuple {w, G} is analyzed using discrete-time linear system theory. The convergent condition of APO algorithm and corresponding parameter selection guidelines are derived. The simulation results show that the convergent condition is effective in guiding the parameter selection of APO algorithm and can help to explain why those parameters work well.

Published in:

Modelling, Identification and Control (ICMIC), The 2010 International Conference on

Date of Conference:

17-19 July 2010