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This paper describes the development of a new reference controller framework for fixed and floating offshore wind turbines that greatly facilitates controller tuning and represents standard industry practices. The reference wind turbine controllers that are most commonly cited in the literature have been developed to work with specific reference wind turbines. Although these controllers have provided standard control functionalities, they are often not easy to modify for use on other turbines, so it has been challenging for researchers to run representative, fully dynamic simulations of other wind turbine designs. The Reference Open-Source Controller (ROSCO) has been developed to provide a modular reference wind turbine controller that represents industry standards and performs comparably to or better than existing reference controllers. The formulation of the ROSCO controller logic and tuning processes is presented in this paper. Control capabilities such as tip speed ratio tracking generator torque control, minimum pitch saturation, wind speed estimation, and a smoothing algorithm at near-rated operation are included to provide modern controller features. A floating offshore wind turbine feedback module is also included to facilitate growing research in the floating offshore arena. All of the standard controller implementations and control modules are automatically tuned such that a non-controls engineer or automated optimization routine can easily improve the controller performance. This article provides the framework and theoretical basis for the ROSCO controller modules and generic tuning processes. Simulations of the National Renewable Energy Laboratory (NREL) 5MWreference wind turbine and International Energy Agency 15MW reference turbine on the University of Maine semisubmersible platform are analyzed to demonstrate the controller's performance in both fixed and floating configurations, respectively. The simulation results demonstrate ROSCO's peak shaving routine to reduce maximum rotor thrusts by over 10% compared to the NREL 5MWreference wind turbine controller on the land-based turbine and to reduce maximum platform pitch angles by nearly 30% when using the platform feedback routine instead of a more traditional low-bandwidth controller. [ABSTRACT FROM AUTHOR]

In order to obtain the initial condition of the traffic accident and reconstruct the collision process of motorcycle and car in reverse, this paper proposes a multi-objective optimisation algorithm based on the combination of NSGA-II and surrogate model. Aiming at a collision accident between a car and motorcycle, a simulation model of the human-vehicle-road coupling system is established. First, select the optimisation variables, optimisation objectives and constraints, define the variable range, and through the Opt-LHD to sample the optimisation variables. Then, obtain and fit the sample data, and take the sample data as the initial conditions into the established motorcycle traffic accident simulation model. Next, obtain the simulation data of the optimisation variables, and establish the response surface model of the optimisation objectives in MATLAB. Finally, using NSGA-II algorithm, the individuals in the population are solved based on the response surface model in turn, the Pareto solution set is obtained, and a group of satisfactory solutions is taken back to the simulation model for verification of the optimisation model, so as to prove the correctness of the satisfactory solution, and then the initial conditions of the collision between the motorcycle and the car are obtained. [ABSTRACT FROM AUTHOR]

In the quest towards coarse-grained potentials and new water models, we present an extension of the force matching technique to parameterize an all-atom force field for rigid water. The methodology presented here allows to improve the matching procedure by first optimizing the weighting exponents present in the objective function. A new gauge for unambiguously evaluating the quality of the fit has been introduced; it is based on the root mean square difference of the distributions of target properties between reference data and fitted potentials. Four rigid water models have been parameterized; the matching procedure has been used to assess the role of the ghost atom in TIP4P-like models and of electrostatic damping. In the former case, burying the negative charge inside the molecule allows to fit better the torques. In the latter, since short-range interactions are damped, a better fit of the forces is obtained. Overall, the best performing model is the one with a ghost atom and with electrostatic damping. The approach shown in this paper is of general validity and could be applied to any matching algorithm and to any level of coarse graining, also for non-rigid molecules. [ABSTRACT FROM AUTHOR]

In this correspondence paper, an underlay cognitive radio multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs’ individual interference constraint. The optimization problem formulated for this is proved to be nonquasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme. [ABSTRACT FROM AUTHOR]

In practice, most projects result in cost overruns and schedule slippage due to poor resource management. This paper presents an approach that aims at reducing project duration and costs by empowering project managers to assess different scenarios. The proposed approach addresses combinatorial modes for tasks, multi-skilled resources, and multiple calendars for resources. A case study reported in the literature is presented to demonstrate the capabilities of this method. As for practical implications, this approach enhances the decision-making process which results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this paper adds empirical evidence to enrich the existing literature, as it highlights relevant issues to model properly the complexity of real-life projects. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive synthesis using the particle swarm optimization (PSO) for implementations of resonator-coupled filters is proposed. The coupling matrix of in-band filtering response is achieved and optimized by the PSO-based synthesis. Meanwhile, the stopband coupling matrix of the filter is predicted based on the combination of extra resonant nodes representing stopband spurious and parasitic effect of input/output ports. In addition, an enhanced accurate prediction of filter performance is calculated by the proposed approach, considering the practical fabrication tolerance on filter design. To verify principles mentioned earlier, various resonator-coupled filters are implemented. The calculation, EM-simulation, and measurement of filters show good agreements in both passband and stopband. [ABSTRACT FROM AUTHOR]

Purpose This paper aims to represent an improved whale optimization algorithm (WOA) based on a Lévy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Lévy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.Design/methodology/approach In this paper, an improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems.Findings It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.Originality value An improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is first proposed. [ABSTRACT FROM AUTHOR]

In this paper we examine the different formulations of Gillespie’s stochastic simulation algorithm (SSA) [D. Gillespie, J. Phys. Chem. 81, 2340 (1977)] with respect to computational efficiency, and propose an optimization to improve the efficiency of the direct method. Based on careful timing studies and an analysis of the time-consuming operations, we conclude that for most practical problems the optimized direct method is the most efficient formulation of SSA. This is in contrast to the widely held belief that Gibson and Bruck’s next reaction method [M. Gibson and J. Bruck, J. Phys. Chem. A 104, 1876 (2000)] is the best way to implement the SSA for large systems. Our analysis explains the source of the discrepancy. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN. [ABSTRACT FROM PUBLISHER]

Bayesian optimization has become a widely used tool in the optimization and machine learning communities. It is suitable to problems as simulation/optimization and/or with an objective function computationally expensive to evaluate. Bayesian optimization is based on a surrogate probabilistic model of the objective whose mean and variance are sequentially updated using the observations and an “acquisition” function based on the model, which sets the next observation at the most “promising” point. The most used surrogate model is the Gaussian Process which is the basis of well-known Kriging algorithms. In this paper, the authors consider the pump scheduling optimization problem in a Water Distribution Network with both ON/OFF and variable speed pumps. In a global optimization model, accounting for time patterns of demand and energy price allows significant cost savings. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make pump scheduling optimization computationally challenging, even for small Water Distribution Networks. The well-known EPANET simulator is used to compute the energy cost associated to a pump schedule and to verify that hydraulic constraints are not violated and demand is met. Two Bayesian Optimization approaches are proposed in this paper, where the surrogate model is based on a Gaussian Process and a Random Forest, respectively. Both approaches are tested with different acquisition functions on a set of test functions, a benchmark Water Distribution Network from the literature and a large-scale real-life Water Distribution Network in Milan, Italy. [ABSTRACT FROM AUTHOR]

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases. [ABSTRACT FROM AUTHOR]

Driven by increasing user requirements and expectations, the fast development of telecommunications networks brings new challenging optimization problems. One of them is routing and spectrum allocation (RSA) of three types of network flows (unicast, anycast, multicast) in elastic optical networks (EONs) implementing dedicated path protection (DPP). In the paper, we model this problem as integer linear programming (ILP) and we introduce two new optimization approaches--a dedicated heuristic algorithm and a column generation (CG)-based method. Then, relying on extensive simulations, we compare algorithm performance with reference methods and evaluate CG efficiency in detail. The results show that the proposed CG method significantly outperforms reference algorithms and achieves results very close to optimal ones (the average distance to optimal results was at most 2.1%). [ABSTRACT FROM AUTHOR]

The deployment of large-scale UAV clusters for cluster advantages will lead to high competition and excessive congestion of spectrum resources, which results in mutual interference. This paper investigates the interference-aware online spectrum access problem for multicluster Flying Ad-Hoc Network (FANET) under different network topologies, i.e., the locations of UAV clusters are varying. First, the problem is formulated as a data-assisted multistage channel access game with the goal of mitigating interference of all UAV clusters and decreasing channel switching cost during each slot. As we prove that the game is an exact potential game that guarantees at least one pure-strategy Nash equilibrium and interference-aware online channel preserving based concurrent best response (IOCPCBR) algorithm, an online distributed algorithm is proposed to achieve the desirable solution. Finally, the simulation results demonstrate the validity and effectiveness of the multistage channel access game as well as IOCPCBR algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel definition of opposite path. Its core feature is that the sequence of candidate paths and the distances between adjacent nodes in the tour are considered simultaneously. In a sense, the candidate path and its corresponding opposite path have the same (or similar at least) distance to the optimal path in the current population. Based on an accepted framework for employing opposition-based learning, Oppositional Biogeography-Based Optimization using the Current Optimum, called COOBBO algorithm, is introduced to solve traveling salesman problems. We demonstrate its performance on eight benchmark problems and compare it with other optimization algorithms. Simulation results illustrate that the excellent performance of our proposed algorithm is attributed to the distinct definition of opposite path. In addition, its great strength lies in exploitation for enhancing the solution accuracy, not exploration for improving the population diversity. Finally, by comparing different version of COOBBO, another conclusion is that each successful opposition-based soft computing algorithm needs to adjust and remain a good balance between backward adjacent node and forward adjacent node. [ABSTRACT FROM AUTHOR]

Efficiency of hydraulic solvers for the simulation of flows and pressures in water distribution systems (WDSs) is very important, especially in the context of optimization and risk analysis problems, where the hydraulic simulation has to be repeated many times. Among the methods used for hydraulic solvers, the most prominent nowadays is the global gradient algorithm (GGA), based on a hybrid node-loop formulation. Previously, another method based just on loop flow equations was proposed, which presents the advantage that it leads to a system matrix that is in most cases much smaller than in the GGA method, but has also some disadvantages, mainly a less sparse system matrix and the fact that introducing some types of valves requires the redefinition of the set of network loops initially defined. The contribution of this paper is to present solutions for overcoming the mentioned disadvantages of the method based on loop flow equations. In particular, efficient procedures are shown for selecting the network loops so as to achieve a highly sparse matrix and methods are presented to incorporate check valves and automatic control valves while avoiding the need to redefine the loops initially selected. [ABSTRACT FROM AUTHOR]

Information carried by an image can be distorted due to different image processing steps introduced by different electronic means of storage and communication. Therefore, development of algorithms which can automatically assess a quality of the image in a way that is consistent with human evaluation is important. In this paper, an approach to image quality assessment (IQA) is proposed in which the quality of a given image is evaluated jointly by several IQA approaches. At first, in order to obtain such joint models, an optimisation problem of IQA measures aggregation is defined, where a weighted sum of their outputs, i.e., objective scores, is used as the aggregation operator. Then, the weight of each measure is considered as a decision variable in a problem of minimisation of root mean square error between obtained objective scores and subjective scores. Subjective scores reflect ground-truth and involve evaluation of images by human observers. The optimisation problem is solved using a genetic algorithm, which also selects suitable measures used in aggregation. Obtained multimeasures are evaluated on four largest widely used image benchmarks and compared against state-of-the-art full-reference IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing measures. [ABSTRACT FROM AUTHOR]

In practice, many systems exhibit load‐sharing behavior, where the surviving components share the total load imposed on the system. Different from general systems, the components of load‐sharing systems are interdependent in nature, in such a way that when one component fails, the system load has to be shared by the remaining components, which increases the failure rate or degradation rate of the remaining components. Because of the load‐sharing mechanism among components, parameter estimation and reliability assessment are usually complicated for load‐sharing systems. Although load‐sharing systems with components subject to sudden failures have been intensely studied in literatures with detailed estimation and analysis approaches, those with components subject to degradation are rarely investigated. In this paper, we propose the parameter estimation method for load‐sharing systems subject to continuous degradation with a constant load. Likelihood function based on the degradation data of components is established as a first step. The maximum likelihood estimators for unknown parameters are deduced and obtained via expectation‐maximization (EM) algorithm considering the nonclosed form of the likelihood function. Numerical examples are used to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization. [ABSTRACT FROM AUTHOR]

In this paper, a Shuffled Frog Leaping Algorithm (SFLA) is proposed to address the Profit-Based Unit Commitment (PBUC) problem under deregulation. The PBUC problem is one of the major tasks for a power producer and is a highly complex, multi-constrained, nonlinear optimization problem. Some of the recently developed algorithms like GA, PSO, BBO, ACO, etc. are available to solve this complex problem, but so far there has been no such ideal technique which can completely handle the computational time and the large dimensionality of the problem. In this scenario, an attempt is made to solve this problem using SFLA. The problem is formulated as a biobjective optimization problem with profit maximization of generation companies as one objective and emission level limitation of generating units as second objective. The proposed algorithm is tested on IEEE 10 unit 10 hours load demand as input data for simulation using MATLAB. The results are compared with GA and PSO. From the results, it is observed that SFLA effectively handles the dimension of the problem and achieves maximum profit and minimum production cost with less computational time. [ABSTRACT FROM AUTHOR]

To improve computational precision for parameter optimization of the van Genuchten model in simulating moisture movement in environment protection, an improved gray-encoded evolution algorithm based on chaos cluster is proposed, in which an initial population is generated by chaotic mapping, and the searching range is automatically renewed with the excellent individuals by chaos cluster operation. Its efficiency is verified experimentally. The results indicate that the absolute error by the improved gray-encoded evolution algorithm based on chaos cluster decreases by 7.52% and 40.40%, respectively, and the relative error decreases by 12.65% and 49.95%, respectively, compared to those by the standard binary-encoded evolution algorithm, and the particle swarm optimization algorithm. Improved gray-encoded evolution algorithm based on chaos cluster has higher precision and it is good for the global optimization in the practical parameter optimization in environment system. [ABSTRACT FROM AUTHOR]

This paper presents a research that investigates solutions and algorithms for determining the optimum machine layout served by autonomous material handling system, like mobile robot or automated guided vehicle. Unlike previous works which solved the layout problem by optimizing the distance between facilities, in this paper the machine layout is addressed based on optimizing the travel time of the material handling system. The proposed approach can include boundary kinematic constraints of vehicle while optimizing the objective function such as velocity, acceleration, orientation, and trajectory curvature. The nonlinear constrained model is transformed to unconstrained problem using penalty method. Then, a simulated annealing-based algorithm is used to search for the optimum locations of machines among all possible feasible layouts. The simulation results showed that the proposed approach was efficient enough to use in real factories due to including a vehicle path planner integrated in an overall layout design scheme that involves searching of vehicle control parameters. [ABSTRACT FROM AUTHOR]

Detecting communities in a network can be helpful to comprehend its structure and understand its function. The detecting-communities approach, in essence, is a model of classification and belongs to the scope of social methodology. In this paper, we view the community description of a network as a lossy compression of that network's information, and develop an information-theoretic foundation accordingly for the concept of community in networks. We present an optimization model and its algorithm to identify the communities by finding an optimal compression of the network. We also illustrate the availability of this approach by an artificial example, compare its accuracy and algorithm complexity with the other classical approaches of this field by a series of simulated networks with different parameters, and demonstrate its application in a real-world network. The tests show that the proposed method is a good one for detecting the communities and finding the proper community number in the unweighted and weighted networks. [ABSTRACT FROM AUTHOR]

In order to understand the role of crossover in differential evolution, theoretical analysis and comparative study of crossover in differential evolution are presented in this paper. Two new crossover methods, namely consecutive binomial crossover and non-consecutive exponential crossover, are designed. The probability distribution and expectation of crossover length for binomial and exponential crossover used in this paper are derived. Various differential evolution algorithms with different crossover methods including mutation-only differential evolution are comprehensively compared at system level instead of parameter level. Based on the theoretical analysis and simulation results, the effect of crossover on the reliability and efficiency of differential evolution algorithms is discussed. Some insights are revealed. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework. [ABSTRACT FROM AUTHOR]

In this paper, the reconstruction of perfectly electric conductor (PEC) scatterers with transverse electric (TE) wave illumination is investigated using the continuous-type subspace-based optimization method (SOM). The PEC scatterers can be of arbitrary locations, shapes and quantities and no prior information about the scatterers is required in the reconstructions. Compared with the transverse magnetic (TM) counterpart, the TE case is not only mathematically more complex, but also numerically more demanding in the sense that the triangular basis is used to represent induced electric current. Moreover, under better mesh strategy and more strict objective function, numerical simulations for arbitrary PEC scatterers generate satisfactory results and are well robust against noise. The algorithm is also examined under the experimental scattering data and still obtains good reconstruction results. [ABSTRACT FROM PUBLISHER]

This paper attempts to determine the maximum number of storage and retrieval requests that can be handled by automated warehousing systems under physical and operational constraints. The system is formulated as a stochastic constrained optimization problem and an algorithm is developed for its solution. Due to the stochastic nature of the operation of the system, a simulation model is constructed to evaluate the system for various values of its parameters. The optimization routine interacts with the simulation model and systematically moves towards the optimum solution. The problem is studied under various levels of constraints. Also several levels of the risk of violation of constraints are examined. [ABSTRACT FROM AUTHOR]

This paper presents the development of a new miniaturized and compact GCPW-fed slotted rectangular antenna structure reader for wideband UHF RFID applications. The optimized proposed antenna is suitable to operate a large frequency-band range from 0.8GHz to 1.3GHz with a bandwidth of 500MHz with a return loss less than -10dB. The antenna is based on a 1.6mm thickness FR4 epoxy substrate with a reduce dimensions compared to the simple rectangular antenna and size of proposed antenna is 47*40mm2. The new design consists of a compact rectangular patch with symmetric Ushaped slots and I-shaped include a partial ground plan and fed by 50 Grounded coplanar line. The antenna parameters have been investigated and optimized by using CST Microwave Studio. To validate the CST Microwave Studio results before the antenna achievement, we have conducted another study by using ADS. The final circuit achieved, measured and validated. Experimental results show that the proposed antenna has good radiation characteristics and operating in UHF-RFID applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sensitivity-based analysis to study the nonlinear behavior under nonexpected utility with probability distortions (or 'distorted utility' for short). We first discover the 'monolinearity' of distorted utility, which means that after properly changing the underlying probability measure, distorted utility becomes locally linear in probabilities, and the derivative of distorted utility is simply an expectation of the sample path derivative under the new measure. From the monolinearity property, simulation algorithms for estimating the derivative of distorted utility can be developed, leading to gradient-based search algorithms for the optimum of distorted utility. We then apply the sensitivity-based approach to the portfolio selection problem under distorted utility with complete and incomplete markets. For the complete markets case, the first-order condition is derived and optimal wealth deduced. For the incomplete markets case, a dual characterization of optimal policies is provided; a solvable incomplete market example with unhedgeable interest rate risk is also presented. We expect this sensitivity-based approach to be generally applicable to optimization problems involving probability distortions. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) networks impose a significant challenge on delay-sensitive crowdsourcing due to the highly non-deterministic and intermittent network connectivity. Under this setting, the paper investigates a participant recruitment problem in which an initial set of recruited nodes, which we call seeds, need to make an optimal decision on what other nodes to recruit to perform the crowdsourcing task. These seeds face the dilemma that recruiting more nodes increases their own payment but on the other hand also increases the risk of being excluded from the crowdsourcing task. As a first attack to this problem, we propose a dynamic programming algorithm. However, it is a centralized solution and hence the practicality is compromised. Therefore, we introduce two distributed alternatives. One is based on the divide-and-conquer paradigm by first partitioning a network into a set of opportunistic Voronoi cells and then running an optimization algorithm in each cell. The other is a task-splitting scheme which recursively delegates the recruitment task to newly joined nodes. We implemented our proposed solutions on an Android-based prototype and built a testbed using 25 Dell Streak tablets. Our experiments which lasted for 24 days demonstrate that the distributed schemes approximate the theoretical optimum with affordable complexity. Moreover, we conducted simulations with a much larger scale and more diverse settings. The simulation results corroborate the experimental data and confirm that our proposed distributed solutions closely approach the performance of the centralized solution while satisfying the optimization goal under different network configurations. [ABSTRACT FROM AUTHOR]

This paper deals with undiscounted infinite stage two-person zero-sum stochastic games with finite state and action spaces. It was recently shown that such games possess a value. But in general there are no optimal strategies. We prove that for each player there exists a nonempty set of easy initial states, i.e. starting states for which the player possesses an optimal stationary strategy. This result is proved with the aid of facts derived by Bewley and Kohlberg for the limit discount equation for stochastic games. [ABSTRACT FROM AUTHOR]

This article takes up the issue of optimization to consider the relationship between predictive algorithms and platform user experience. Corporate data analytic practices increasingly rely on machine learning algorithms that apply models to user behaviors, producing "knowledge" about users that can be bought and sold. This article considers the opacity of algorithms today in relation to optimization. Using a conceptual apparatus that draws from the study of cultural techniques, the following argues that optimization—the task of finding a sufficient solution to a well-defined problem—makes use of models to simulate possible answers to problems around the incomputablity of behavior. Tracing a set of examples that deal with the problem of predicting behavior—the "minimum point" problem, John von Neumann's automata theory, and the Facebook pixel—optimization is characterized by a shift from statistical model making towards predictive and algorithmic techniques. This shift is seen within the context of the decline of Cold War rationality towards the embeddedness of "intelligent" algorithms across technoculture. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, nonlinear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. The proposed algorithm interprets the problem as an unexplored terrain for climbing. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The efficiency of the proposed algorithm is demonstrated by application to conventional test functions. In this paper, the simulation results show that skewing does not necessarily reduce the cogging torque but may cause it to increase for certain pole-arc to pole-pitch ratio. The developed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one. [ABSTRACT FROM AUTHOR]

This paper presents an optimized path generation algorithm for direction parallel milling, which is commonly used in the roughing and finishing stages. First, a geometrically efficient tool path generation algorithm using an intersection points graph is introduced. Second, the generated tool path is modified as an optimized tool path that maintains a constant material removal rate to achieve a constant cutting force and avoid chatter vibration, and the results are verified. Additional tool path segments are appended to the basic tool path through a pixel-based simulation technique. The algorithm is implemented for two-dimensional contiguous end milling operations with flat end mills, and cutting tests are conducted by measuring the spindle current, which reflects the changing machining situations, to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an online procedure for the automatic search of the maximum-torque-per-ampere operating region for a synchronous reluctance motor. The algorithm is based on a signal-injection method with a random-based perturbation pattern applied to a common direct-torque-controlled drive. Among motor parameters, only the stator resistance is required to perform the automatic procedure. Simulations and experimental results are presented in the paper, demonstrating the benefits of the proposed algorithm. The solution is easily extended to any ac drive. [ABSTRACT FROM PUBLISHER]

Most existing scheduling algorithms for multimedia traffic incode-division multiple-access (CDMA) networks have been designed under the assumption that the physical layer operates with a bounded bit error rate (BER) such that the packet error rate (PER) in the link layer is also bounded. However, there is a tradeoff between the PER and the packet drop rate (PDR) caused by delay violations, i.e., by slightly degrading the PER, more packets can be sent, thus lowering the PDR. In this paper, we present a new cross-layer enhancement to optimize the cross-layer qualityof-service (QoS) parameter - the packet loss rate (PLR) - that accounts for both the PER and the PDR. Furthermore, we study the fairness issue in terms of the PLR experienced by different users. Then, we propose a novel algorithm to schedule uplink multimedia traffic in time-division multicode CDMA networks, which optimizes the PLR experienced by each user in each frame, subject to fair distribution of packet losses between users according to the proposed cross-layer enhancement and fairness constraint. Simulation results show that the proposed algorithm provides fairness in packet losses among users of different traffic classes and achieves superior performance over two previous algorithms in terms of increasing the system capacity and reducing the PLR and average delay because of the cross-layer enhancement. [ABSTRACT FROM AUTHOR]

Adaptive infinite impulse response (IIR) filters have shown their worth in a wide range of practical applications. Because the error surface of IIR filters is multimodal in most cases, global optimization techniques are required for avoiding local minima. In this paper, we employ a global optimization algorithm, Quantum-behaved particle swarm optimization (QPSO) that was proposed by us previously, and itsmutated version in the design of digital IIR filter. The mechanism inQPSO is based on the quantum behaviour of particles in a potential well and particle swarm optimization (PSO) algorithm. QPSO is characterized by fast convergence, good search ability, and easy implementation. The mutated QPSO (MuQPSO) is proposed in this paper by using a random vector in QPSO to increase the randomness and to enhance the global search ability. Experimental results on three examples show that QPSO andMuQPSO are superior to genetic algorithm (GA), differential evolution (DE) algorithm, and PSO algorithm in quality, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]

This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually reducing population size is proposed in this paper. It improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm. The proposed modification is tested on commonly used benchmark problems for unconstrained optimization and compared with other optimization methods such as Evolutionary Algorithms and Evolution Strategies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast and efficient adaptive transmission algorithm for the Mobile WiMAX system. In our algorithm, we select the position of the boundary between the downlink and uplink subframes, the positions of the service, flows within the frame, the coding and modulation schemes, and the transmission power values. Our algorithm gains high spectral efficiency, satisfies quality-of-service (QoS) requirements for the service flows, and has low computational complexity. The algorithm includes a frame boundary position optimization procedure, downlink and uplink optimization procedures, as well as a procedure that selects a transmission power value that enables satisfaction of the QoS requirements. All these procedures are novel, efficient, and fast. We evaluate the adaptive transmission algorithm by performing a system-level simulation of the Mobile WiMAX system with multisector topology. We show that our algorithm has high spectral efficiency and low computational complexity, enabling QoS provisioning. [ABSTRACT FROM AUTHOR]

This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them we propose a potentially nonconvex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region, localization occurs by globally exponentially convergent gradient descent in the noise free case. Exponential convergence in the noise free case represents practical convergence as it ensures graceful performance degradation in the presence of noise. These results guide the deployment of sensors/anchors so that small subsets can be made responsible for practical localization in geographical areas determined by our approach. [ABSTRACT FROM AUTHOR]

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Developing Takagi-Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show, that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi-Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the properties of the sampled version of the fictitious play algorithm, familiar from game theory, for games with identical payoffs, and propose a heuristic based on fictitious play as a solution procedure for discrete optimization problems of the form max {u(y) y = (y1,…yn ∈ y1 x … x yn} i.e., in which the feasible region is a Cartesian product of finite sets y1 i ∈ N = (1, …, n). The contributions of this paper are twofold. In the first part of the paper, we broaden the existing results on convergence properties of the fictitious play algorithm on games with identical payoffs to include an approximate fictitious play algorithm that allows for errors in players 'best replies. Moreover, we introduce sampling-based approximate fictitious play that possesses the above convergence properties, and at the same time provides a computationally efficient method for implementing fictitious play. In the second part of the paper, we motivate the use of algorithms based on sampled fictitious play to solve optimization problems in the above form with particular focus on the problems in which the objective function u(·) comes from a "black box," such as a simulation model, where significant computational effort is required for each function evaluation. [ABSTRACT FROM AUTHOR]

Several recent papers have proposed the use of grids for solving unconstrained optimisation problems. Grid-based methods typically generate a sequence of grid local minimisers which converges to stationary points under mild conditions. In this paper the location and number of grid local minimisers is calculated for strictly convex quadratic functions in two dimensions with certain types of grids. These calculations show it is possible to construct a grid with an arbitrary number of grid local minimisers. The furthest of these can be an arbitrary distance from the quadratic's minimiser. These results have important implications for the design of practical grid-based algorithms. Grids based on conjugate directions do not suffer from these problems. For such grids only the grid points closest (depending on the choice of metric) to the minimiser are grid local minimisers. Furthermore, conjugate grids are shown to be reasonably stable under mild perturbations so that in practice, only approximately conjugate grids are required. [ABSTRACT FROM AUTHOR]