Paper Info
Title
Incorporating kin selection in simulated annealing algorithm and its performance evaluation
Abstract
In this article, a new operator namely the kin selection operator is introduced, which significantly improves the performance of conventional simulated annealing (SA) algorithm. Inspired by a phenomenon of the same name observed in the evolutionary system, the proposed approach offers better solutions by sacrificing a solution for its `kin'. By doing so, it ensures an efficiently guided, thorough search in the neighbourhood of the best solution. Theoretical analysis is performed to show that the basic tenets of SA hold for the proposed methodology as well. Moreover, such a methodology also provides enhanced probability of survival of critical information patterns in the solution space. Experimental comparison with SA on a large number of function optimization problems is performed. In order to validate the performance of the proposed methodology over the conventional SA, a well known NP hard problem that deals with multi-level lot-sizing and scheduling problem in a PCB manufacturing firm is taken as an illustrative example.
Year
DOI
Venue
2004
10.1016/S0377-2217(03)00251-0
European Journal of Operational Research
Keywords
Field
DocType
Kin selection,Simulated annealing (SA),Multi-level lot-sizing and scheduling
Simulated annealing,Mathematical optimization,Job shop scheduling,Adaptive simulated annealing,Function optimization,Operator (computer programming),Kin selection,Operations management,Mathematics
Journal
Volume
Issue
ISSN
158
1
0377-2217
Citations 
PageRank 
References 
6
0.74
2
Authors
3
Name
Order
Citations
PageRank
Alok Singh120117.15
Atreya Mukherjee271.11
M. K. Tiwari31240115.22