Genetic annealing with efficient strategies to improve the performance for the NP-hard and routing problems

problem which cannot be solved in polynomial time for asymptotically large values of and travelling salesman problem (TSP) is important in operations research and theoretical computer science. In this paper a balanced combination of genetic algorithm and simulated annealing has been applied. To impr...

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Bibliographic Details
Published in:Journal of experimental & theoretical artificial intelligence 2015-11, Vol.27 (6), p.779-788
Main Authors: Eswarawaka, Rajesh, Noor Mahammad, S.K., Eswara Reddy, B.
Format: Article
Language:English
Subjects:
Asymptotic properties
Computer networks
crossover operator
Efficiency
genetic algorithm
Genetic algorithms
inver-over operator
Lin-Kernighan algorithm
Mathematical models
Operators
Optimization
Performance enhancement
Problem solving
Routing
Routing (telecommunications)
selection operator
simulated annealing
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Summary:problem which cannot be solved in polynomial time for asymptotically large values of and travelling salesman problem (TSP) is important in operations research and theoretical computer science. In this paper a balanced combination of genetic algorithm and simulated annealing has been applied. To improve the performance of finding an optimal solution from huge search space, we have incorporated the use of tournament and rank as selection operators, and inver-over operator mechanism for crossover and mutation. This proposed technique is applied for some routing resource problems in a chip design process and a best optimal solution was obtained, and the TSP appears as a sub-problem in many areas and is used as a benchmark for many optimisation methods.
ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2015.1020624