New meta-heuristic for combinatorial optimization problems: intersection based scaling
Journal of Computer Science and Technology
Improving EAX with restricted 2-opt
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Finding the optimal gene order in displaying microarray data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Application notes: memetic mission management
IEEE Computational Intelligence Magazine
A new evolutionary algorithm using shadow price guided operators
Applied Soft Computing
An efficient local search algorithm for k-median problem
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Fast EAX algorithm considering population diversity for traveling salesman problems
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Accelerating 2-opt and 3-opt Local Search Using GPU in the Travelling Salesman Problem
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Large scale parallel iterated local search algorithm for solving traveling salesman problem
Proceedings of the 2012 Symposium on High Performance Computing
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In this paper an evolutionary algorithm for the traveling salesman problem is proposed. The key idea is to enhance the ability of exploration and exploitation by incorporating global search with local search. A new local search, called the neighbor-join (NJ) operator, is proposed to improve the solution quality of the edge assembly crossover (EAX) considered as a global search mechanism in this paper. Our method is applied to 15 well-known traveling salesman problems with numbers of cities ranging from 101 to 3038 cities. The experimental results indicate that the neighbor-join operator is very competitive with related operators surveyed in this paper. Incorporating the NJ into the EAX significantly outperforms the method incorporating 2-opt into the EAX for some hard problems. For each test instance the average value of solution quality stays within 0.03% from the optimum. For the notorious hard problem att532, it is able to find the optimum solution 23 times in 30 independent runs.