Design of reliable communication networks: A hybrid ant colony optimization algorithm

Dengız, Berna; Altıparmak, Fulya; Belgin, Önder

doi:10.1080/07408170903039836

Cited by 26 publications

(16 citation statements)

References 32 publications

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“…ACO algorithms have been applied for the reliability optimization of series-parallel systems [44], also including quantity discounts on the redundant components [45], and network optimization by embedding a Cellular Automata approach combined with Monte Carlo simulation for network availability assessment [46], within a multi-objective ACO search engine [47]. ACO has also been applied in hybrid form with Simulated Annealing (SA), called ACO SA, for the design of communication networks [48], where the design problem is to find the optimal network topology for which the total cost is a minimum and the all-terminal reliability is not less than a given level of reliability.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The evolution of system reliability optimization

Coit¹,

Zio²

2019

Reliability Engineering & System Safety

139

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Research pertaining to system reliability optimization has been categorized chronologically into three eras.  The first era, the era of mathematical programming involved rigorous optimization methods, yet the problems are not always realistic.  The second era, the era of pragmatism, involved expanding the problem domain to include a broader range of problems, and more realistic problems.  The final and current era, the era of active reliability improvement involves dynamic reliability optimization models responding to changing conditions and data.  System reliability optimization problems remain challenging, but important, while both the problems and corresponding solution methods evolve.

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Section: Accepted Manuscriptmentioning

confidence: 99%

The evolution of system reliability optimization

Coit¹,

Zio²

2019

Reliability Engineering & System Safety

139

View full text Add to dashboard Cite

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“…ACO is effective for solving Non-deterministic Polynomial (NP) hard discrete optimization problems, and has been successfully applied to a number of scientific and engineering problems, including grid-based deployment for wireless sensor networks [ 22 , 23 , 24 ]. ACO is also applied to the topology optimization [ 25 , 26 ] and routing algorithm [ 27 , 28 ] for wireless networks. An ACO algorithm coupled with a local search heuristic is proposed in [ 29 ] to deploy a WSN under a certain reliability constraint at the minimum deployment cost.…”

Section: Related Workmentioning

confidence: 99%

Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme

Duan

Lin

Cai

et al. 2020

Sensors

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The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.

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“…BDD structure is a compact, implicit representation of the entire set of the functioning and failing network states. Dengiz, et al [23] proposed a hybrid approach based on Ant Colony Optimization and Simulated Annealing, called ACO-SA, for the NTD-CR problem for networks with up to 50 nodes. Note that the results in [7] for the related Dynamic programming approach show that the DP techniques produced better results as compared to the BDD approach in [22] and ACO-SA approach in [23].…”

Section: Related Workmentioning

confidence: 99%

“…As a summary, the existing algorithms that generate approximation solutions are mainly based on meta-heuristic techniques, such as Genetic Algorithm [10,11,14,22], Neural Network [5,21], Swarm Particle [24] [21], Simulated Annealing (SA) [17,18] and Ant Colony Optimization (ACO) [23]. While the meta heuristic-based algorithms may significantly reduce time complexity, they still require numerous iterations to converge and thus use a considerable computational effort to produce near optimal solutions [23]. Therefore, a more time efficient heuristic approach that can produce better results is still needed, especially for use in large scale networks.…”

Section: Related Workmentioning

confidence: 99%

On Minimizing Cost of Reliable Network Topological Design using a Practical DP Approach

Elshqeirat¹

2020

IJATCSE

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This paper addresses an NP-hard problem called NTD-CR that using dynamic programming (DP) scheme. This problem referred to as Network Topology Design with minimum Cost subject to a Reliability constraint. It designs a minimal-cost communication network topology that satisfies a pre-defined (s, t) terminal reliability constraint. Our DP approach, called DPCR-L, mainly select the set of possible links to be deleted from the original network to generate an optimal Network Topology. The NTD-CR design problem aims to find a Network Topology that has minimal cost with the required reliability for the network. Five link-ordering criteria are proposed to improve the performance of DPCR-L. Each greedy heuristic order allows DPCR-L to enumerate the selected deleted links, which improves the time complexity while producing near optimal topology. Extensive Simulations based on different benchmark networks of various sizes are used to compare DPCR-L with existing state-of-the-art techniques and show the merits of using the ordering methods, and the effectiveness of our algorithm. Our simulations show that DPCR-L produces 93% optimal results. Interestingly, of the non-optimal results, DPCR-L produces a network with reliability no worse than 5.38% off optimal, and most of the non-optimal results have a lower cost than that for optimal up to 0.17%. Typically, for the most of the network topologies, DPCR-L generates NT with the same or better 2-terminal reliability measure and speeds up its running time up to 31.71%. Furthermore, simulation results on large size networks show that DPCR-L speeds up the process with up to (47.28%) compared to the recent existing approach. Finally, the results present the applicability of DPCR-L on networks containing a large number of links and demonstrated better performance and computationally more efficient than other existing algorithms.

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Design of reliable communication networks: A hybrid ant colony optimization algorithm

Cited by 26 publications

References 32 publications

The evolution of system reliability optimization

The evolution of system reliability optimization

Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme

On Minimizing Cost of Reliable Network Topological Design using a Practical DP Approach

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