Title
A Markov Chain Monte Carlo Algorithm for the Quadratic Assignment Problem Based on Replicator Equations
Abstract
This paper proposes an optimization algorithm for the Quadratic Assignment Problem (QAP) based on replicator equations. If the growth rate of a replicator equation is composed of the performance index and the constraints of the QAP suitably, by increasing the value of the control parameter in the growth rate, the equilibrium solutions which correspond to the feasible solutions of the QAP become stable in order from the one with smaller value of the performance index. Based on the characteristics of the system, the following optimization algorithm is constructed; the control parameter is set so that the equilibrium solutions corresponding to the feasible solutions with smaller values of the performance index become stable, and then in the solution space of the replicator equations, a Markov chain Monte Carlo algorithm is carried out. The proposed algorithm is applied to many problem instances in the QAPLIB. It is revealed that the algorithm can obtain the solutions equivalent to the best known solutions in short time. Especially, for some large scale instances, the new solutions with the same cost as the best known solutions are obtained.
Year
DOI
Venue
2001
10.1007/3-540-44668-0_21
ICANN
Keywords
Field
DocType
markov chain monte carlo,monte carlo algorithm,equilibrium solution,performance index,smaller value,control parameter,replicator equations,quadratic assignment problem,growth rate,feasible solution,known solution,solutions equivalent,replicator equation
Mathematical optimization,Monte Carlo method,Performance index,Quadratic assignment problem,Replicator equation,Markov chain,Markov chain monte carlo algorithm,Combinatorial optimization,Optimization algorithm,Mathematics
Conference
Volume
ISSN
ISBN
2130
0302-9743
3-540-42486-5
Citations 
PageRank 
References 
1
0.37
4
Authors
3
Name
Order
Citations
PageRank
Takehiro Nishiyama110.71
Kazuo Tsuchiya26811.71
Katsuyoshi Tsujita37713.62