A hybrid ant colony optimization approach for the design of reliable networks

Dengız, Berna; Altıparmak, Fulya; Belgin, Önder

doi:10.1109/cec.2007.4424595

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Indeed two of the legacy ant algorithms examined in this paper, namely ACS [1], [12], [13] and MMAS [16], use 3-opt to improve ant tours. More recent efforts include using simulated annealing [23], random walks [24], tabu searches [24], H-method [25], minimum spanning trees [26], forward checking and back-tracking [27]. Repairing of infeasible solutions can also be considered a form of local search [28].…”

Section: A Local Searchmentioning

confidence: 99%

WoLF Ant

Peterson

Mayer

Cousin

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Ant colony optimization (ACO) algorithms can generate quality solutions to combinatorial optimization problems. However, like many stochastic algorithms, the quality of solutions worsen as problem sizes grow. In an effort to increase performance, we added the variable step size off-policy hill-climbing algorithm called PDWoLF (Policy Dynamics Win or Learn Fast) to several ant colony algorithms: Ant System, Ant Colony System, Elitist-Ant System, Rank-based Ant System, and Max-Min Ant System. Easily integrated into each ACO algorithm, the PDWoLF component maintains a set of policies separate from the ant colony's pheromone. Similar to pheromone but with different update rules, the PDWoLF policies provide a second estimation of solution quality and guide the construction of solutions. Experiments on large traveling salesman problems (TSPs) show that incorporating PDWoLF with the aforementioned ACO algorithms that do not make use of local optimizations produces shorter tours than the ACO algorithms alone.

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Section: A Local Searchmentioning

confidence: 99%

WoLF Ant

Peterson

Mayer

Cousin

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Optimization of multiple objectives and topological design of data networks using genetic algorithm

Dasgupta

Biswas

Bhar

2012

2012 1st International Conference on Recent Advances in Information Technology (RAIT)

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Improved Approach for Maximizing Reliability in Fault Tolerant Networks

Kaushik¹,

Kaur²,

Kohli³

2013

J. Adv. Comput. Intell. Intell. Inform.

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The objective of this paper is to present a novelmethod for achievingmaximumreliability in fault-tolerant optimal network design when networks have variable size. Reliability calculation is a most important and critical component when fault-tolerant optimal network design is required. A network must be supplied with certain parameters that guarantee proper functionality and maintainability in worse-case situations. Many alternative methods for measuring reliability have been stated in the literature for optimal network design. Most of these methods, mentioned in the literature for evaluating reliability, may be analytical and simulation-based. These methods provide significant ways for computing reliability when a network has a limited size. Significant computational effort is also required for growing variable-sized networks. A novel neural network method is therefore presented to achieve significant high reliability in fault-tolerant optimal network design in highly growing variable networks. This paper compares simulation-based analytical methods with improved learning rate gradient descent-based neural network methods. Results show that improved optimal network design with maximum reliability is achievable by a novel neural network at a manageable computational cost.

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A hybrid ant colony optimization approach for the design of reliable networks

Cited by 3 publications

References 36 publications

WoLF Ant

WoLF Ant

Optimization of multiple objectives and topological design of data networks using genetic algorithm

Improved Approach for Maximizing Reliability in Fault Tolerant Networks

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