Paper Info
Title
Fast Annealing Genetic Algorithm For Multi-Objective Optimization Problems
Abstract
In this article, we propose a fast annealing genetic algorithm (FAGA), based on the principle of the minimal free energy in statistical physics, for solving multi-objective optimization problems. The novelties of FAGA are: (1) providing a new fitness assignment strategy by combining Pareto-dominance relation and Gibbs entropy, (2) introducing a new criterion for selection of new individuals to maintain the diversity of the population. We make many experiments to measure the performance of the proposed FAGA, and estimate its convergence rate for a number of test problems. Simulation results show that the FAGA is a very fast and effective algorithm.
Year
DOI
Venue
2005
10.1080/0020716042000272557
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
Keywords
DocType
Volume
multi-objective optimization, genetic algorithin, statistical physics, simulated annealing, metropolis criterion
Journal
82
Issue
ISSN
Citations 
8
0020-7160
4
PageRank 
References 
Authors
0.59
5
2
Name
Order
Citations
PageRank
Xiufen Zou127225.44
Lishan Kang277591.11