Computers and Operations Research
A genetic algorithm for distributed system topology design
Computers and Industrial Engineering - Collection of papers on Computer-Integrated Manufacturing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A heuristic algorithm for the network design problem
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
An algorithm for the network design problem based on the maximum entropy method
AMERICAN-MATH'10 Proceedings of the 2010 American conference on Applied mathematics
A productivity-oriented methodology for local area network design in industrial environments
Computer Networks: The International Journal of Computer and Telecommunications Networking
Efficient Optimization of Reliable Two-Node Connected Networks: A Biobjective Approach
INFORMS Journal on Computing
An approach to stabilize interdomain routing protocol after failure
Computers and Electrical Engineering
Computers and Electrical Engineering
Software Survey: Distributed job scheduling based on Swarm Intelligence: A survey
Computers and Electrical Engineering
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Network design problem is a well-known NP-hard problem which involves the selection of a subset of possible links or a network topology in order to minimize the network cost subjected to the reliability constraint. To overcome the problem, this paper proposes a new efficiency algorithm based on the conventional ant colony optimization (ACO) to solve the communication network design when considering both economics and reliability. The proposed method is called improved ant colony optimizations (IACO) which introduces two addition techniques in order to improve the search process, i.e. neighborhood search and re-initialization process. To show its efficiency, IACO is applied to test with three different topology network systems and its results are compared with those obtained results from the conventional approaches, i.e. genetic algorithm (GA), tabu search algorithm (TSA) and ACO. Simulation results, obtained these test problems with various constraints, shown that the proposed approach is superior to the conventional algorithms both solution quality and computational time.