Paper Info
Title
A novel method for solving min-max problems by using a modified particle swarm optimization
Abstract
In this paper, a method for solving min-max problems, especially for finding a solution which satisfies “min-max = max-min” condition, by using a modified particle swarm optimization (PSO) algorithm, is proposed. According to recent development in computer science, multi-point global search methods, most of which are classified into evolutionary computation and/or meta-heuristic methods, have been proposed and applied to various types of optimization problems. However, applications of them to min-max problems have been scarce despite their theoretical and practical importance. Since direct application of evolutionary computation methods to min-max problems wouldn't work effectively, a modified PSO algorithm for solving them is proposed. The proposed method is designed: (1) to approximate the minimized and maximized functions of min-max problems by using a finite number of search points; and, (2) to obtain one of “min-max = max-min” solutions by finding the minimum of the maximized function and the maximum of the minimized function. Numerical examples demonstrate the usefulness of the proposed method.
Year
DOI
Venue
2011
10.1109/ICSMC.2011.6083984
Systems, Man, and Cybernetics
Keywords
Field
DocType
evolutionary computation,minimax techniques,particle swarm optimisation,evolutionary computation,max-min solution,meta-heuristic method,min-max problem,modified particle swarm optimization,multipoint global search method,Lagrange multiplier method,game theory,min-max problem,particle swarm optimization (PSO)
Particle swarm optimization,Mathematical optimization,Finite set,Computer science,Iterative method,Lagrange multiplier,Evolutionary computation,Multi-swarm optimization,Artificial intelligence,Imperialist competitive algorithm,Optimization problem,Machine learning
Conference
ISSN
ISBN
Citations 
1062-922X
978-1-4577-0652-3
2
PageRank 
References 
Authors
0.37
4
3
Name
Order
Citations
PageRank
Kazuaki Masuda174.21
Kenzo Kurihara275.23
Eitaro Aiyoshi35211.55