By Topic

An experimental study for multi-objective optimization by particle swarm with graph based archive

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)
Yamamoto, M. ; Dept. of Inf. & Phys. Sci., Osaka Univ., Suita, Japan ; Uchitane, T. ; Hatanaka, T.

Particle Swarm Optimization is a stochastic multi point search algorithm mimicking social behaviors of animals, such as bird flock. Recently, many researchers pay attentions to Particle Swarm Optimization applying to multi-objective problems. In multi-objective optimization problems, it is desired that solutions cover Pareto-optimal front widely and uniformly. Generally multi-objective particle swarm optimization employs archiving method to store non-dominated solutions which are found in searching and the guide is selected from the archived solutions. In this paper, we consider a topology-based archive updating and guide selection in multi-objective particle swarm optimization in order to keep balance between exploration and exploitation. In the proposed method, each particle has an archive (sub-archive) and the sub-archive is updated by itself and its neighborhood particles. Since it takes some iterations that members in the sub-archive of the particle affect the behaviors of all particle, this method prevents early convergence and the diversity of solutions are mainlined. The performances of the proposed methods with regular graph topology are evaluated by using well known benchmark problems for the evolutionary multi-objective optimization algorithms.

Published in:

SICE Annual Conference (SICE), 2012 Proceedings of

Date of Conference:

20-23 Aug. 2012