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Continuous learning automata solutions to the capacity assignment problem

IEEE TRANSACTIONS ON COMPUTERS 2000年第6期49卷 608-620页

作者： Oommen, BJ Roberts, TD Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

The capacity assignment (CA) problem focuses on finding the best possible set of capacities for the links that satisfies the traffic requirements in a prioritized network while minimizing the cost. Most approaches consider a single class of packets flowing through the network, but. in reality, different classes of packets with different packet lengths and priorities are transmitted over the networks. In this paper, we assume that the traffic consists of different classes of packets with different average packet lengths and priorities. We shall look at three different solutions to this problem. Marayuma and Tang [9] proposed a single algorithm composed of several elementary heuristic procedures. Levi and Ersoy [8] introduced a simulated annealing approach that produced substantially better results. In this paper, we introduce a new method which uses continuous learning automata to solve the problem. Our new schemes produce superior results when compared with either of the previous solutions and is, to our knowledge, currently the best known solution.

关键词： learning automata capacity assignment problem network design

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DC programming approach for multicommodity network optimization problems with step increasing cost functions

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JOURNAL OF GLOBAL OPTIMIZATION 2002年第1-4期22卷 205-232页

作者： An, LTH Tao, PD Natl Inst Appl Sci Rouen Math Lab Modelling Appl Optimizat & Operat Res Grp F-76131 Mont St Aignan France

We address a class of particularly hard-to-solve combinatorial optimization problems, namely that of multicommodity network optimization when the link cost functions are discontinuous step increasing. Unlike usual approaches consisting in the development of relaxations for such problems (in an equivalent form of a large scale mixed integer linear programming problem) in order to derive lower bounds, our d.c.(difference of convex functions) approach deals with the original continuous version and provides upper bounds. More precisely we approximate step increasing functions as closely as desired by differences of polyhedral convex functions and then apply DCA (difference of convex function algorithm) to the resulting approximate polyhedral d.c. programs. Preliminary computational experiments are presented on a series of test problems with structures similar to those encountered in telecommunication networks. They show that the d.c. approach and DCA provide feasible multicommodity flows x(*) such that the relative differences between upper bounds (computed by DCA) and simple lower bounds r :=(f(x(*))-LB)/{f(x(*))} lies in the range [4.2 %, 16.5 %] with an average of 11.5 %, where f is the cost function of the problem and LB is a lower bound obtained by solving the linearized program (that is built from the original problem by replacing step increasing cost functions with simple affine minorizations). It seems that for the first time so good upper bounds have been obtained.

关键词： multicommodity network flows step increasing cost function capacity assignment problem flow assignment problem mixed-integer linear program d.c. (difference of convex functions) program polyhedral d.c. program relaxation techniques DCA (d.c. algorithm)

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Optimal Energy-Delay Scheduling for Energy-Harvesting WSNs With Interference Channel via Negatively Correlated Search

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IEEE INTERNET OF THINGS JOURNAL 2020年第3期7卷 1690-1703页

作者： Jiao, Dongbin Yang, Peng Fu, Liqun Ke, Liangjun Tang, Ke Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Xian 710049 Peoples R China Southern Univ Sci & Technol Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Xiamen Univ Minist Educ Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Minist Educ Key Lab Underwater Acoust Commun & Marine Informa Xiamen 361005 Peoples R China

Network resource allocation is an important issue for designing energy-harvesting wireless sensor networks (EH-WSNs). This article considers the capacity assignment problem in EH-WSNs with the interference channel for fixed data and energy flow topologies. We focus on the optimal data rates, power allocations, and energy transfers, minimizing the total network delay for the network. We first consider a simplified model where the data flow is fixed on each data link and optimizes transmit power at each sensor node for a single energy harvest in a time slot. However, the optimization problem is nonconvex, making it difficult to find the optimal solution. Unlike the most traditional methods that approximate the original optimization problem as a convex optimization problem by considering the relatively high signal-to-interference-plus-noise ratio (SINR), this article aims to directly solve the original nonconvex formulation by employing a powerful evolutionary algorithm, i.e., negatively correlated search (NCS). Then, we investigate the joint optimization problem of capacity and flow for the entire EH-WSNs, and develop a novel multiobjective NCS algorithm (MOEA/D-NCS) to deal with the complicated nonlinear constraints and optimize the data rates, power allocations, and energy transfer simultaneously, so as to minimize the total network delay. The numerical results demonstrate that solving the nonconvex problem with approximated approach is a good alternative for solving the approximated convex problem with accurate optimization approaches;the joint optimization of capacity and flow is a good solution for EH-WSNs;and the scheme of partial transmission for data flow is an advantage in respect of decreasing the network delay. The solution of this article could also be beneficial to other complex optimization problems in the wireless network design.

关键词： capacity assignment problem energy harvesting interference channel negatively correlated search (NCS) wireless sensor networks

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Optimal Energy-Delay Scheduling using Improved Beetle Antennae Search (BAS) for Energy-Harvesting WSNs

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期126卷 2533-2556页

作者： Kumar, Battina Srinuvasu Santhi, S. G. Narayana, S. Annamalai Univ Dept Comp Sci & Engn Annamalainagar 608002 Tamil Nadu India Gudlavalleru Engn Coll Dept Comp Sci & Engn Gudlavalleru 521356 India

Optimal energy delay scheduling for capacity allocation problems in interference channel energy harvesting wireless sensor networks (EH-WSN) address of static data streams as well as power surveying. The goal is to diminish the general regression of network. However, the optimization problem is not curved, which makes it difficult to obtain optimal solution. The major goal of this document is to directly address that original non-convex formulation with enhanced Beetle Antennae Search (BAS) algorithm competently gets optimal solution. The simulations in delay, delivery ratio, drop, energy, network life, overload, and throughput are performed;the predictable result shows the improved BAS algorithm performs better convex approximation system. This plan is helpful for other complex optimization problems in WSN.

关键词： Energy harvesting Wireless sensor networks Interference channel capacity assignment problem Beetle Antenna Search

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Optimal Energy-Delay Scheduling for Energy Harvesting WSNs via Negatively Correlated

Optimal Energy-Delay Scheduling for Energy Harvesting WSNs v...

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IEEE International Conference on Communications (IEEE ICC)

作者： Jiao, Dongbin Yang, Peng Fu, Liqun Ke, Liangjun Tang, Ke Xi An Jiao Tong Univ Sch Elect & Informat Engn State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China Xiamen Univ Minist Educ Key Lab Underwater Acoust Commun & Marine Informa Sch Informat Sci & Engn Xiamen 361005 Fujian Peoples R China

ISBN: (纸本)9781538680889

Optimal energy-delay scheduling for capacity assignment problem in energy harvesting wireless sensor networks (EH-WSNs) with interference channel is addressed for fixed data flows and energy topologies. We formulate the optimization problem for a single time slot and multiple time slots, respectively. We focus on the optimal data rates, power allocations and energy transfers for the optimization problem. The objective is to minimize the total network delay. However, the optimization problem is non-convex, making it difficult to find the optimal solution. Unlike the most traditional methods that approximate the original optimization problem as a convex optimization problem by considering the relatively high Signal-to-Interference-plus-Noise Ratio (SINR), this paper aims to directly solve the original non-convex formulation by employing a powerful evolutionary algorithm, i.e., Negatively Correlated Search (NCS). The simulations under both no-energy-transfer scenario and energy-transfer scenario are carried out, demonstrating that solving the non-convex problem with approximated approach is a good alternative to solving the approximated convex problem with accurate optimization approaches. This idea could also be beneficial to other complex optimization problems in the wireless networks design.

关键词： Energy harvesting wireless sensor networks interference channel capacity assignment problem Negatively Correlated Search

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Artificial intelligent applications for estimating flow network reliability

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AIN SHAMS ENGINEERING JOURNAL 2023年第8期14卷

作者： Hassan, Moatamed Refaat Alkhalaf, Salem Hemeida, Ashraf Mohamed Ahmed, Mahrous Mahmoud, Eman Aswan Univ Comp Sci Branch Fac Sci Dept Math Aswan 81528 Egypt Qassim Univ Coll Sci & Arts Ar Rass Dept Comp Ar Rass 52571 Saudi Arabia Aswan Univ Fac Energy Engn Dept Elect Engn Aswan 81528 Egypt Taif Univ Coll Engn Elect Engn Dept Taif 21944 Saudi Arabia Aswan Univ Fac Sci Dept Math Aswan 81528 Egypt

Artificial intelligence (AI), often known as machine learning, is a powerful tool for solving engineering problems. The evaluation of the network reliability of a flow network is a NP-hard problem, with compu-tational effort growing exponentially with the number of nodes and arcs in the network. Also, the com-ponents assignment issue is NP-hard, and the computational effort increases with the number of available components. Many candidate solutions are typically examined during optimal components or optimal capacity assignment, each requiring reliability calculation. Consequently, this paper proposes an artificial neural network (ANN) predictive model to evaluate the flow network reliability. The neural network is one of the artificial intelligence tools constructed, trained, and validated using the maximum capacity of each component input and the network reliability as the target. The proposed ANN model pro-vides empirical proof that neural networks can accurately estimate reliability by modeling the connection between the maximum capacities of network components and the reliability value.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Artificial intelligent Flow network Components assignment problem capacity assignment problem Reliability evaluation ANN

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Robust capacity assignment Solutions for Telecommunications Networks with Uncertain Demands

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NETWORKS 2013年第4期62卷 255-272页

作者： Babonneau, F. Vial, J. -P. Klopfenstein, O. Ouorou, A. Swiss Fed Inst Technol Lausanne EPFL Econ & Environm Management Lab Lausanne Switzerland Orange Labs Res F-92794 Issy Les Moulineaux France

We consider the capacity planning of telecommunications networks with linear investment costs and uncertain future traffic demands. Transmission capacities must be large enough to meet, with a high quality of service (QoS), the range of possible demands, after adequate routings of the traffic on the created network. We use the robust optimization methodology to balance the need for a given QoS with the cost of investment. Our model assumes that the traffic for each individual demand fluctuates in an interval around a nominal value. We use a refined version of affine decision rules based on a concept of demand proximity to model the routings as affine functions of the demand realizations. We then give a probabilistic analysis assuming the random variables follow a triangular distribution. Finally, we perform numerical experiments on network instances from Survivable fixed telecommunication Network Design Library (SNDlib) and measure the quality of the solutions by simulation. Copyright (c) 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 62(4), 255-272 2013

关键词： capacity assignment problem telecommunications networks robust optimization

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Optimal Energy-Delay in Energy Harvesting Wireless Sensor Networks with Interference Channels

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SENSORS 2019年第4期19卷 785-785页

作者： Jiao, Dongbin Ke, Liangjun Liu, Shengbo Chan, Felix T. S. Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen 361005 Peoples R China Xiamen Univ Key Lab Underwater Acoust Commun & Marine Informa Minist Educ Xiamen 361005 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hung Hom Hong Kong Peoples R China

In this work, we investigate the capacity allocation problem in the energy harvesting wireless sensor networks (WSNs) with interference channels. For the fixed topologies of data and energy, we formulate the optimization problem when the data flow remains constant on all data links and each sensor node harvests energy only once in a time slot. We focus on the optimal data rates, power allocations and energy transfers between sensor nodes in a time slot. Our goal is to minimize the total delay in the network under two scenarios, i.e., no energy transfer and energy transfer. Furthermore, since the optimization problem is non-convex and difficult to solve directly, by considering the network with the relatively high signal-to-interference-plus-noise ratio (SINR), the non-convex optimization problem can be transformed into a convex optimization problem by convex approximation. We attain the properties of the optimal solution by Lagrange duality and solve the convex optimization problem by the CVX solver. The experimental results demonstrate that the total delay of the energy harvesting WSNs with interference channels is more than that in the orthogonal channel;the total network delay increases with the increasing data flow for the fixed energy arrival rate;and the energy transfer can help to decrease the total delay.

关键词： energy harvesting energy transfer wireless sensor networks interference channel convex approximation capacity assignment problem Lagrange duality

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