The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Branka Vucetic - One of the best experts on this subject based on the ideXlab platform.
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simulated annealing based wireless sensor network localization with flip ambiguity mitigation
Vehicular Technology Conference, 2006Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase simulated annealing based localization (SAL) algorithm to address the issue. simulated annealing (SA) is a technique for combinatorial optimization problems and it is robust against being trapped into local minima. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed scheme gives accurate and consistent location estimates of the nodes and mitigate errors due to flip ambiguities.
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simulated annealing based wireless sensor network localization
Journal of Computers, 2006Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:In this paper, we describe a novel localization algorithm for ad hoc wireless sensor networks. Accurate selforganization and localization capability is a highly desirable characteristic of wireless sensor networks. Many researchers have approached the localization problem from different perspectives. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase localization method based on the simulated annealing technique to address the issue. simulated annealingis a technique for combinatorial optimization problems and unlike the gradient search method, it is robust against being trapped into local minima. In this paper we show that our simulated annealing based localization method can be used in ad hoc wireless sensor networks to estimate the locationof nodes accurately. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have been affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed novel scheme gives accurate and consistent location estimates of the nodes, and mitigate errors due to flip ambiguity. The performance of the proposed algorithm is better than the performance of some well-known schemes such as DVhop method and convex optimization based semi-definite programming method.
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simulated annealing based localization in wireless sensor network
Local Computer Networks, 2005Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:In sensor networks, the information obtained from sensors are meaningless without the location information. In this paper, we propose a simulated annealing based localization (SAL) scheme for wireless sensor networks. simulated annealing (SA) is used to estimate the approximate solution to combinatorial optimization problems. The SAL scheme can bring the convergence out of the local minima in a controlled fashion. Simulation results show that this scheme gives accurate and consistent location estimates of the nodes
Anushiya A Kannan - One of the best experts on this subject based on the ideXlab platform.
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simulated annealing based wireless sensor network localization with flip ambiguity mitigation
Vehicular Technology Conference, 2006Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase simulated annealing based localization (SAL) algorithm to address the issue. simulated annealing (SA) is a technique for combinatorial optimization problems and it is robust against being trapped into local minima. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed scheme gives accurate and consistent location estimates of the nodes and mitigate errors due to flip ambiguities.
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simulated annealing based wireless sensor network localization
Journal of Computers, 2006Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:In this paper, we describe a novel localization algorithm for ad hoc wireless sensor networks. Accurate selforganization and localization capability is a highly desirable characteristic of wireless sensor networks. Many researchers have approached the localization problem from different perspectives. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase localization method based on the simulated annealing technique to address the issue. simulated annealingis a technique for combinatorial optimization problems and unlike the gradient search method, it is robust against being trapped into local minima. In this paper we show that our simulated annealing based localization method can be used in ad hoc wireless sensor networks to estimate the locationof nodes accurately. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have been affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed novel scheme gives accurate and consistent location estimates of the nodes, and mitigate errors due to flip ambiguity. The performance of the proposed algorithm is better than the performance of some well-known schemes such as DVhop method and convex optimization based semi-definite programming method.
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simulated annealing based localization in wireless sensor network
Local Computer Networks, 2005Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:In sensor networks, the information obtained from sensors are meaningless without the location information. In this paper, we propose a simulated annealing based localization (SAL) scheme for wireless sensor networks. simulated annealing (SA) is used to estimate the approximate solution to combinatorial optimization problems. The SAL scheme can bring the convergence out of the local minima in a controlled fashion. Simulation results show that this scheme gives accurate and consistent location estimates of the nodes
Guoqiang Mao - One of the best experts on this subject based on the ideXlab platform.
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simulated annealing based wireless sensor network localization with flip ambiguity mitigation
Vehicular Technology Conference, 2006Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase simulated annealing based localization (SAL) algorithm to address the issue. simulated annealing (SA) is a technique for combinatorial optimization problems and it is robust against being trapped into local minima. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed scheme gives accurate and consistent location estimates of the nodes and mitigate errors due to flip ambiguities.
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simulated annealing based wireless sensor network localization
Journal of Computers, 2006Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:In this paper, we describe a novel localization algorithm for ad hoc wireless sensor networks. Accurate selforganization and localization capability is a highly desirable characteristic of wireless sensor networks. Many researchers have approached the localization problem from different perspectives. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase localization method based on the simulated annealing technique to address the issue. simulated annealingis a technique for combinatorial optimization problems and unlike the gradient search method, it is robust against being trapped into local minima. In this paper we show that our simulated annealing based localization method can be used in ad hoc wireless sensor networks to estimate the locationof nodes accurately. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have been affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed novel scheme gives accurate and consistent location estimates of the nodes, and mitigate errors due to flip ambiguity. The performance of the proposed algorithm is better than the performance of some well-known schemes such as DVhop method and convex optimization based semi-definite programming method.
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simulated annealing based localization in wireless sensor network
Local Computer Networks, 2005Co-Authors: Anushiya A Kannan, Guoqiang Mao, Branka VuceticAbstract:In sensor networks, the information obtained from sensors are meaningless without the location information. In this paper, we propose a simulated annealing based localization (SAL) scheme for wireless sensor networks. simulated annealing (SA) is used to estimate the approximate solution to combinatorial optimization problems. The SAL scheme can bring the convergence out of the local minima in a controlled fashion. Simulation results show that this scheme gives accurate and consistent location estimates of the nodes
R. Romero - One of the best experts on this subject based on the ideXlab platform.
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Parallel simulated annealing applied to long term transmission network expansion planning
IEEE Transactions on Power Systems, 1997Co-Authors: R. A. Gallego, A. B. Alves, Antonella Monticelli, R. RomeroAbstract:The simulated annealing optimization technique has been successfully applied to a number of electrical engineering problems, including transmission system expansion planning. The method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Moreover, it has the ability to provide solutions arbitrarily close to an optimum (i.e. it is asymptotically convergent) as the cooling process slows down. The drawback of the approach is the computational burden: finding optimal solutions may be extremely expensive in some cases. This paper presents a parallel simulated annealing (PSA) algorithm for solving the long-term transmission network expansion planning problem. A strategy that does not affect the basic convergence properties of the sequential simulated annealing algorithm have been implemented and tested. The paper investigates the conditions under which the parallel algorithm is most efficient. The parallel implementations have been tested on three example networks: a small 6-bus network; and two complex real-life networks. Excellent results are reported in the test section of the paper: in addition to reductions in computing times, the PSA algorithm proposed in the paper has shown significant improvements in solution quality for the largest of the test networks
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transmission system expansion planning by simulated annealing
IEEE Transactions on Power Systems, 1995Co-Authors: R. Romero, R. A. Gallego, Antonella MonticelliAbstract:This paper presents a simulated annealing approach to the long term transmission expansion planning problem which is a hard, large scale combinatorial problem. The proposed approach has been compared with a more conventional optimization technique based on mathematical decomposition with a zero-one implicit enumeration procedure. Tests have been performed on three different systems. Two smaller systems for which optimal solutions are known have been used to tune the main parameters of the simulated annealing process. The simulated annealing method has then been applied to a larger example system for which no optimal solutions are known: as a result an entire family of interesting solutions have been obtained with costs about 7% less than the best solutions known for that particular example system.
Shokri Z Selim - One of the best experts on this subject based on the ideXlab platform.
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integrating genetic algorithms tabu search and simulated annealing for the unit commitment problem
IEEE Transactions on Power Systems, 1999Co-Authors: A H Mantawy, Y L Abdelmagid, Shokri Z SelimAbstract:This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction phase of the genetic algorithm. A simulated annealing method is used to accelerate the convergence of the genetic algorithm by applying the simulated annealing test for all the population members. A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. The fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the proposed algorithm, a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum, and the premature convergence of the genetic algorithm. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results showed the superiority of the solutions obtained compared to genetic algorithms, tabu search and simulated annealing methods, and to two exact algorithms.
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a simulated annealing algorithm for unit commitment
IEEE Transactions on Power Systems, 1998Co-Authors: A H Mantawy, Y L Abdelmagid, Shokri Z SelimAbstract:This paper presents a simulated annealing algorithm (SAA) to solve the unit commitment problem (UCP). New rules for randomly generating feasible solutions are introduced. The problem has two subproblems: a combinatorial optimization problem; and a nonlinear programming problem. The former is solved using the SAA while the latter problem is solved via a quadratic programming routine. Numerical results showed an improvement in the solutions costs compared to previously obtained results.
