Optimal Placement Problem Research Articles

Static timing analysis for modeling QoS in networks-on-chip

Networks-on-chip (NoCs) are used in a growing number of SoCs and multi-core processors. Because messages compete for the NoC’s shared resources, quality of service and resource allocation are major concerns for system designers. In particular, a model for the properties of packet delivery through the network is desirable. We present a methodology for packet-level static timing analysis in NoCs. Our methodology quickly and accurately gauges the performance parameters of a virtual-channel wormhole NoC without simulation. The network model can handle any topology, link capacities, and buffer sizes. It provides per-flow delay analysis that is orders-of-magnitude faster than simulation while being significantly more accurate than prior static modeling techniques. Using a carefully derived and reduced Markov chain, the model can statically represent the dynamic network state. Usage of the model in a placement optimization problem is shown as an example application.

Comparison of different optimum criteria for sensor placement in lattice towers

SUMMARYPlacing sensors at appropriate locations is an important task in the dynamic assessment of the mechanical systems, and its role is central in structural health monitoring. All sensors have to be placed on the structure so that all sought system features be obtained from the experimental tests. Sensor network design is rather diffused in laboratory applications, but there are scarce investigations on real civil structures, and they are not so well documented. In order to give a contribution in this framework, the present paper deals with the optimal sensor placement problem in lattice towers. With reference to six of the most diffused existing optimum criteria and according to mechanical and energetic formulations, several sensor network configurations are calculated for two broadcasting antennas, adopted as benchmark cases of studies. The final topological configurations of the networks are compared, and the most relevant differences are discussed. Moreover, the information content behaviour is computed to investigate the connection between sensor network topology and its ability to withstand the presence of undesired signals (e.g., noise) during the measurements. The results illustrated here may be useful for sensor network design for this special class of structures. Copyright © 2010 John Wiley & Sons, Ltd.

An aggregated optimization model for multi-head SMD placements

In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads, minimizing the number of nozzle exchanges, and improving handling class. The handling class which specifies the traveling speed of the robot arm, to the best of our knowledge, has been for the first time incorporated in an optimization model. While the MIP produces an optimal planning for batches of components, a new sequencing heuristics is developed in order to determine the final sequence of component placements based on the outputs of the MIP. This two-stage approach guarantees a good feasible solution to the multi-head SMD placement optimization problem. The computational performance is examined using real industrial data.

Surface Reconstruction Based on Hierarchical Floating Radial Basis Functions

Abstract In this paper we address the problem of optimal centre placement for scattered data approximation using radial basis functions (RBFs) by introducing the concept of floating centres. Given an initial least‐squares solution, we optimize the positions and the weights of the RBF centres by minimizing a non‐linear error function. By optimizing the centre positions, we obtain better approximations with a lower number of centres, which improves the numerical stability of the fitting procedure. We combine the non‐linear RBF fitting with a hierarchical domain decomposition technique. This provides a powerful tool for surface reconstruction from oriented point samples. By directly incorporating point normal vectors into the optimization process, we avoid the use of off‐surface points which results in less computational overhead and reduces undesired surface artefacts. We demonstrate that the proposed surface reconstruction technique is as robust as recent methods, which compute the indicator function of the solid described by the point samples. In contrast to indicator function based methods, our method computes a global distance field that can directly be used for shape registration.

Pareto-based evolutionary computational approach for wireless sensor placement

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks’ compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.

Modelling and solving optimal placement problems in wireless sensor networks

In this work, the optimal sensor displacement problem in wireless sensor networks is addressed. It is assumed that a network, consisting of independent, collaborative and mobile nodes, is available. Starting from an initial configuration, the aim is to define a specific sensors displacement, which allows the network to achieve high performance, in terms of energy consumption and travelled distance. To mathematically represent the problem under study, different innovative optimization models are proposed and defined, by taking into account different performance objectives. An extensive computational phase is carried out in order to assess the behaviour of the developed models in terms of solution quality and computational effort. A comparison with distributed approaches is also given, by considering different scenarios.

An Aggregated Optimization Model for Multi-Head SMD Placements

In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads, minimizing the number of nozzle exchanges, and improving handling class. The handling class which specifies the traveling speed of the robot arm, to the best of our knowledge, has been for the first time incorporated in an optimization model. While the MIP produces an optimal planning for batches of components, a new sequencing heuristics is developed in order to determine the final sequence of component placements based on the outputs of the MIP. This two-stage approach guarantees a good feasible solution to the multi-head SMD placement optimization problem. The computational performance is examined using real industrial data.

Optimal PMU placement using Iterated Local Search

An essential tool for power system monitoring is state estimation. Using PMUs can greatly improve the state estimation process. However, for state estimation, the PMUs should be placed appropriately in the network. The problem of optimal PMU placement for full observability is analysed in this paper. The objective of the paper is to minimise the size of the PMU configuration while allowing full observability of the network. The method proposed initially suggests a PMU distribution which makes the network observable. The Iterated Local Search (ILS) metaheuristic is then used to minimise the size of the PMU configuration needed to observe the network. The algorithm is tested on IEEE test networks with 14, 57 and 118 nodes and compared to the results obtained in previous publications.

Heuristic Approaches with Energy Management for Node Placement in wireless Sensor Networks

ABSTRACT In Wireless Sensor Network, Device placement is a key factor for determining the coverage, connectivity, and cost along with lifetime. Managing the sensor nodes is not much easier while comparing mobile Ad ¬ Hoc Networks. But the same approach can be implemented to manage the WSN. Addressing the management of the whole network is omitted and a probabilistic scheme where only a subset of nodes is managed is provided for light ¬ weight and efficient management. Relay node placement in heterogeneous WSN are formulated using a generalized node placement optimization problem to minimize the network cost with lifetime constraint, and connectivity. Based on the constraints two scenarios are used. In the first scenario relay nodes are not energy constrained, and in the second scenario all nodes are energy limited. As an optimal solution a two-phase approach is proposed. The placement of the first phase relay nodes (FPRN), which are directly connected to Sensor Nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Some of the heuristic approaches available are Nearest¬ to ¬ BS¬ First algorithm (NTBF), Max¬ Residual¬ Capacity¬ First algorithm (MRCF) and Best¬ Effort¬ Relaying algorithm (BER). Our contribution is centered on a distributed self organizing management algorithm at the application layer by organizing the management plane by extracting spatiotemporal components and by selecting manager nodes with several election mechanisms applied to wireless sensor nodes. Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided.

Optimal placement of UV-based communications relay nodes

We consider a constrained optimization problem with mixed integer and real variables. It models optimal placement of communications relay nodes in the presence of obstacles. This problem is widely encountered, for instance, in robotics, where it is required to survey some target located in one point and convey the gathered information back to a base station located in another point. One or more unmanned aerial or ground vehicles (UAVs or UGVs) can be used for this purpose as communications relays. The decision variables are the number of unmanned vehicles (UVs) and the UV positions. The objective function is assumed to access the placement quality. We suggest one instance of such a function which is more suitable for accessing UAV placement. The constraints are determined by, firstly, a free line of sight requirement for every consecutive pair in the chain and, secondly, a limited communication range. Because of these requirements, our constrained optimization problem is a difficult multi-extremal problem for any fixed number of UVs. Moreover, the feasible set of real variables is typically disjoint. We present an approach that allows us to efficiently find a practically acceptable approximation to a global minimum in the problem of optimal placement of communications relay nodes. It is based on a spatial discretization with a subsequent reduction to a shortest path problem. The case of a restricted number of available UVs is also considered here. We introduce two label correcting algorithms which are able to take advantage of using some peculiarities of the resulting restricted shortest path problem. The algorithms produce a Pareto solution to the two-objective problem of minimizing the path cost and the number of hops. We justify their correctness. The presented results of numerical 3D experiments show that our algorithms are superior to the conventional Bellman-Ford algorithm tailored to solving this problem.

Placement Optimization for Yield Improvement of Switched-Capacitor Analog Integrated Circuits

Capacitor mismatch can generally result from two sources of error: random mismatch and systematic mismatch. Random mismatch is caused by process variation, while systematic mismatch is mainly due to an asymmetrical layout and processing gradients. A common centroid structure may be used to reduce systematic mismatch errors, but not random mismatch errors. Based on the spatial correlation model, this paper formulates the placement optimization problem of analog circuits using switched-capacitor techniques. A placement with higher correlation coefficients of the unit capacitors results in a higher acceptance rate, or chip yield. This paper proposes a heuristic algorithm that quickly and automatically derives the placement of the unit capacitors with the highest, or near-highest, correlation coefficients for yield improvement. Results show that the resultant placement derived from the proposed algorithm achieves better yield improvement than that from a common centroid approach. The proposed heuristic algorithm can be applied for any arbitrary capacitor ratios, i.e., more than two capacitors.

Design-Optimization and Material Selection for a Proximal Radius Fracture-Fixation Implant

The problem of optimal size, shape, and placement of a proximal radius-fracture fixation-plate is addressed computationally using a combined finite-element/design-optimization procedure. To expand the set of physiological loading conditions experienced by the implant during normal everyday activities of the patient, beyond those typically covered by the pre-clinical implant-evaluation testing procedures, the case of a wheel-chair push exertion is considered. Toward that end, a musculoskeletal multi-body inverse-dynamics analysis is carried out of a human propelling a wheelchair. The results obtained are used as input to a finite-element structural analysis for evaluation of the maximum stress and fatigue life of the parametrically defined implant design. While optimizing the design of the radius-fracture fixation-plate, realistic functional requirements pertaining to the attainment of the required level of the devise safety factor and longevity/lifecycle were considered. It is argued that the type of analyses employed in the present work should be: (a) used to complement the standard experimental pre-clinical implant-evaluation tests (the tests which normally include a limited number of daily-living physiological loading conditions and which rely on single pass/fail outcomes/decisions with respect to a set of lower-bound implant-performance criteria) and (b) integrated early in the implant design and material/manufacturing-route selection process.

Harmony Search Algorithm for Optimal Placement Problem of Distributed Generations

This paper presents a application of Harmony Search (HS) algorithm for optimal placement of distributed generations(DGs) in distribution systems. In optimization procedure, the HS algorithm denotes the searching ability for the global optimal solution with simple coding of the iteration procedure, and shows the fast convergence characteristics for getting solutions. The HS algorithm is tested on 9 buses and 69 buses distribution systems, and the results prove its effectiveness to determine appropriate placement points of DGs and reducing amount of active power without the occurrence of any mis-determination in selection of its capacity.

Efficient heuristic algorithm used for optimal capacitor placement in distribution systems

An efficient heuristic algorithm is presented in this work in order to solve the optimal capacitor placement problem in radial distribution systems. The proposal uses the solution from the mathematical model after relaxing the integrality of the discrete variables as a strategy to identify the most attractive bus to add capacitors to each step of the heuristic algorithm. The relaxed mathematical model is a non-linear programming problem and is solved using a specialized interior point method. The algorithm still incorporates an additional strategy of local search that enables the finding of a group of quality solutions after small alterations in the optimization strategy. Proposed solution methodology has been implemented and tested in known electric systems getting a satisfactory outcome compared with metaheuristic methods. The tests carried out in electric systems known in specialized literature reveal the satisfactory outcome of the proposed algorithm compared with metaheuristic methods.

Optimal Placement of Phasor Measurement Units Using Immunity Genetic Algorithm

This paper investigates the application of immunity genetic algorithm (IGA) for the problem of optimal placement of phasor measurement units (PMUs) in an electric power network. The problem is to determine the placement sites of the minimal set of PMUs which makes the system observable. Incorporating immune operator in the canonical genetic algorithm (GA), on the condition of preserving GA's advantages, utilizes some characteristics and knowledge of the problems for restraining the degenerative phenomena during evolution, so as to improve the algorithm efficiency. This type of prior knowledge about some parts of optimal solution exists in the PMU placement problem. So, the IGA is adopted in this paper to solve the problem. Also, a new effect which is preventing from familial reproduction is studied which shows an increase in converging speed. The effectiveness of the proposed method is verified via IEEE standard systems and a realistic large-scale power system.

Optimal Measurement Placement for Static Harmonic State Estimation in the Power Systems based on Genetic Algorithm

In this paper, a method for optimal measurement placement in the problem of static harmonic state estimation in power systems is proposed. At first, for achieving to a suitable method by considering the precision factor of the estimation, a procedure based on Genetic Algorithm (GA) for optimal placement is suggested. Optimal placement by regarding the precision factor has an evident solution, and the proposed method is successful in achieving the mentioned solution. But, the previous applied method, which is called the Sequential Elimination (SE) algorithm, can not achieve to the evident solution of the mentioned problem. Finally, considering both precision and economic factors together in solving the optimal placement problem, a practical method based on GA is proposed. The simulation results are shown an improvement in the precision of the estimation by using the proposed method.

Optimal Meter Placement for Water Distribution System State Estimation

Real-time state estimates (SEs) of nodal demands in a water distribution system (WDS) can be developed using data from a supervisory control and data acquisition (SCADA) system. These estimates provide information for improved operations and customer service in terms of energy consumption and water quality. The SE results in a WDS are significantly affected by measurement characteristics, i.e., meter types, numbers, and topological distributions. The number and type of meters are generally selected prior to a SCADA layout. Thus, selecting measurement locations is critical. The aim of this study is to develop a methodology that optimally locates field measurement sites and leads to more reliable SEs. An optimal meter placement (OMP) problem is posed as a multiobjective optimization form. Three distinctive objectives are formulated: (1) minimization of nodal demand estimation uncertainty; (2) minimization of nodal pressure prediction uncertainty; and (3) minimization of absolute error between demand estimat...

Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithm

AbstractThis paper addresses the problem of optimal placement of wind turbines in a farm on Gokçeada Island located at the north‐east of Aegean Sea bearing full potential of wind energy generation. A multi‐objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright © 2009 John Wiley & Sons, Ltd.

Optimal placements of replicas in a ring network with majority voting protocol

In a distributed database system, data replicas are placed at different locations to achieve high data availability in the presence of link failures. With a majority voting protocol, a location survives for read/write operations if and only if it is accessible to more than half of the replicas. The problem is to find out the optimal placements for a given number of data replicas in a ring network. When the number of replicas is odd, it was conjectured by Hu et al. [X.-D. Hu, X.-H. Jia, D.-Z. Du, D.-Y. Li, H.-J. Huang, Placement of data replicas for optimal data availability in ring networks, J. Parallel Distrib. Comput., 61 (2001) 1412–1424] that every uniform placement is optimal, which is proved by Shekhar and Wu later. However, when the number of replicas is even, it was pointed out by Hu et al. that uniform placements are not optimal and the optimal placement problem may be very complicated. In this paper, we study the optimal placement problem in a ring network with majority voting protocol and an even number of replicas, and give a complete characterization of optimal placements when the number of replicas is not too large compared with the number of locations.

LARGE-SCALE CAPACITOR PLACEMENT FOR DISTRIBUTION SYSTEMS USING HYBRID DIFFERENTIAL EVOLUTION METHOD

This paper introduces a hybrid differential evolution (HDE) method to solve the optimal capacitor placement problem. The optimal capacitor placement problem is to determine the size and location of capacitors to be installed on a radial distribution feeder. It is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints that are commonly solved by employing mathematical programming methods. Due to the discrete nature of reactive compensation devices in the real-scale system, optimal capacitor placement leads to a nonlinear programming problem with integer (discrete) variables. This paper presents a new approach which employs HDE algorithm with integer variables to solve the discrete problem of the optimal capacitor placement. The proposed approach is demonstrated by employing two application examples. Numerical results show that the proposed method can achieve the same optimal solution as the one obtained from the exhaustive search but with much less computational burden. Moreover, comparison reveals that the proposed method is superior to some other commonly used methods in terms of power loss and costs.