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Aiming at the shortcomings of aquila optimization algorithm（AO）, this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation; Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm; Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. Through the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem, The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum. [ABSTRACT FROM AUTHOR]

Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works. [ABSTRACT FROM AUTHOR]

Compressive sensing theory has attracted widespread attention in recent years and sparse signal reconstruction has been widely used in signal processing and communication. This paper addresses the problem of sparse signal recovery especially with non-Gaussian noise. The main contribution of this paper is the proposal of an algorithm where the negentropy and reweighted schemes represent the core of an approach to the solution of the problem. The signal reconstruction problem is formalized as a constrained minimization problem, where the objective function is the sum of a measurement of error statistical characteristic term, the negentropy, and a sparse regularization term, ℓp-norm, for 0 < p < 1. The ℓp-norm, however, leads to a non-convex optimization problem which is difficult to solve efficiently. Herein we treat the ℓp-norm as a serious of weighted ℓ1-norms so that the sub-problems become convex. We propose an optimized algorithm that combines forward-backward splitting. The algorithm is fast and succeeds in exactly recovering sparse signals with Gaussian and non-Gaussian noise. Several numerical experiments and comparisons demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the following paper we present a new type of optimization algorithms adapted for neural network training. These algorithms are based upon sequential operator splitting technique for some associated dynamical systems. Furthermore, we investigate through numerical simulations the empirical rate of convergence of these iterative schemes toward a local minimum of the loss function, with some suitable choices of the underlying hyper-parameters. We validate the convergence of these optimizers using the results of the accuracy and of the loss function on the MNIST, MNIST-Fashion and CIFAR 10 classification datasets. [ABSTRACT FROM AUTHOR]

Task planning involves automating the creation of the routes for vessels with known coordinates in a confined space. The management of vessel release in a given area affects the time required for a vessel to complete its voyage, and maximizing vessel performance involves identifying the shortest route. A key issue in automating the generation of the optimal (shortest) routes is selecting the appropriate mathematical apparatus. This paper considers an optimization method based on a recursive algorithm using Bellman-Ford routing tasks for large dimensions. Unlike other optimization techniques, the proposed method enables the shortest path to be assessed in a network model with a complex topology, even if there are arcs with negative weights. The practical implementation of the modified Floyd algorithm was demonstrated using a sample automated build and using it to calculate a network model with a complex topology, using an iterative procedure for a program prepared in MATLAB. Implementation of the computer model is simple, and unlike existing models, it eliminates restrictions associated with the presence of negative weights and cycles on a network and automates search shortcuts in ground branch functional means in MATLAB. To confirm the accuracy of the obtained results, we performed an example calculation using the network. The proposed algorithm and recursive procedure are recommended for finding energy-efficient solutions during the management of mobile objects on waterways. [ABSTRACT FROM AUTHOR]

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive and accurate algorithm based on circuit model to design reflect-array antennas (RAAs). The proposed algorithm can extract geometry of RAA structure corresponding to radiation parameters without employing any approximation or optimization intervention into the algorithm. Simulation results for some comprehensive examples, verify the validity of the proposed algorithm. This method is simple, fast, accurate, and can be expanded to all species of RAAs with arbitrary geometries. One of the main problems confronted in RAAs is low bandwidth characteristics. To attain wide bandwidth, we exploited the proposed algorithm with combination of multi-layer design of RAAs and subwavelength element. Convergence is observed between simulation and fabrication results, asserting good accuracy in method. In 10–15 GHz band, the maximum gain and -1dB bandwidth of two-layer antenna is 31.4 dB and 18.5%, respectively. [ABSTRACT FROM AUTHOR]

Purpose Dual-rotor wind turbines (DRWTs) are a novel type of wind turbines that can capture more power than their single-rotor counterparts. Because their surrounding flow fields are complex, evaluating a DRWT design requires accurate predictive simulations, which incur high computational costs. Currently, there does not exist a design optimization framework for DRWTs. Since the design optimization of DRWTs requires numerous model evaluations, the purpose of this paper is to identify computationally efficient design approaches.Design/methodology/approach Several algorithms are compared for the design optimization of DRWTs. The algorithms vary widely in approaches and include a direct derivative-free method, as well as three surrogate-based optimization methods, two approximation-based approaches and one variable-fidelity approach with coarse discretization low-fidelity models.Findings The proposed variable-fidelity method required significantly lower computational cost than the derivative-free and approximation-based methods. Large computational savings come from using the time-consuming high-fidelity simulations sparingly and performing the majority of the design space search using the fast variable-fidelity models.Originality/value Due the complex simulations and the large number of designable parameters, the design of DRWTs require the use of numerical optimization algorithms. This work presents a novel and efficient design optimization framework for DRWTs using computationally intensive simulations and variable-fidelity optimization techniques. [ABSTRACT FROM AUTHOR]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

Artificial gas-lift (AGL) technique is commonly used to enhance oil production when the reservoir pressure in wells is not enough to sustain acceptable oil flow rate. However, the gas-lift wells are prone to instability, characterized by regular oscillations of pressure and flow. This phenomenon is known as casing-heading instability. It results in production loss and negative impact on downstream equipment, and has been a challenging problem to both industry and academia. In this paper, a novel concept of optimal tracking control is proposed for stabilization and operating mode transition in gas-lift wells when casing-heading phenomenon occurs. The stability of artificial gas-lift process is ensured by manipulating both gas lift choke and oil production choke, where the openings of both choke valves can vary from fully closed to fully open. Through the simulation of the open-loop system, a stability map of AGL process is produced. Then a trajectory optimization algorithm is developed based on this stability map, which is synthesized with a tracking controller to achieve trajectory optimization control. Also, a nonlinear state observer is designed to ensure estimation of unmeasurable variables. Through simulation studies, the effectiveness of proposed trajectory optimization control is demonstrated. [ABSTRACT FROM AUTHOR]

This paper investigates three-dimensional orbital three-spacecraft-player defense problems. An attacker is about to strike a non-maneuverable asset, while a defender attempts to prevent this attacking in order to protect the asset. It is assumed that both the attacker and the defender have only one chance to maneuver using impulsive thrust. The attacker is not aware of the defender’s participation, while the latter has full information about the former. A hybrid method combined particle swarm optimization with a Newton-Interpolation algorithm is proposed to solve presented orbital defense problems. Numerical results show that the proposed methodology can solve orbital three-player defense problems effectively. Energy consumption of defender is analyzed in detail to tell whether the specified upper bound of defender’s energy is justified. The interesting discovery is the valid departure window of defender in lurk orbit which have important significance for design defender’s strategy in orbital three-player defense problems. [ABSTRACT FROM AUTHOR]

The classic TRIAD was used to obtain the attitude of air vehicles. However, the accuracy was dominated by the sensor noise and the calculation order. To improve that in this paper, a new method based on weighting the vectors summation and difference was proposed. Then both simulation and experiment verified the advantages of the optimized algorithm. [ABSTRACT FROM AUTHOR]

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis of various aspects of small-scale Linear Fresnel Concentrators (LFCs). As we shall see, the influence of the lateral study of the actual absorbed power is very important in this type of LFC. In order to consider the end-effect of the absorber tube, two types of losses need to be taken into account: the end loss and the reflected light loss. Focussing on the optimization of the length and position of the absorber tube, a new mathematical method is presented. The method based on a geometric algorithm allows the straightforward deduction of the optimal design of a small-scale LFC. Numerous numerical simulations are presented for different configurations. This study constitutes the basis of the prototype that is being constructed at a vocational training centre (CIFP-Mantenimiento y Servicios a la Producción) in La Felguera, Asturias, Spain. Asturias, Spain. [ABSTRACT FROM AUTHOR]

This paper presents a new Electromagnetism-like Mechanism (EM) algorithm with Split, Probe and Compare feature (SPC-EM). The proposed algorithm replaces the local search segment of a standard EM with a new search scheme named Split, Probe, and Compare (SPC). A nonlinear equation is designed to systematically and dynamically adjust the length of the probes based on the outcome of the Compare segment in each iteration. Extensive computational simulations and comparisons on 10 different benchmark problems from the literature were carried out. Results show that the new modified mechanism outperformed all other algorithms involved in the benchmarking. We thus conclude that the proposed SPC-EM works well with the designed probe-length tuning equation in solving numerical optimization problems. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we introduce the so-called symplectic discrete singular convolution differentiator (SDSCD) method for structure-preserving modelling of elastic waves. In the method presented, physical space is discretized by the DSCD, whereas an explicit third-order symplectic scheme is used for the time discretization. This approach uses optimization and truncation to form a localized operator. This preserves the fine structure of the wavefield in complex media and avoids non-causal interaction when parameter discontinuities are present in the medium. Theoretically, the approach presented is a structure-preserving algorithm. Also, some numerical experiments are shown in this paper. Elastic wavefield modelling experiments on a laterally heterogeneous medium with high parameter contrasts demonstrate the superior performance of the SDSCD for suppression of numerical dispersion. Long-term computational experiments exhibit the remarkable capability of the approach presented for long-time simulations. Promising numerical results suggest the SDSCD is suitable for high-precision and long-time numerical simulations, as it has structure-preserving property and it can suppress effectively numerical dispersion when coarse grids are used. [ABSTRACT FROM AUTHOR]

As a result of elevated labor costs, a shortage of trades personnel, and a lack of efficient construction methods, many construction companies in western Canada waste primary materials. In general, these firms suffer from a lack of effective construction guidelines and process standardizations. This paper focuses on the use of a mathematical algorithm, referred to here in this paper as CUTEX, which maximizes the use of wood materials for platform-framing residential construction. In particular, CUTEX is designed to reduce waste by generating a cutting list for wood studs and sheathing (oriented strand boards-OSB). A combinatorial analysis algorithm has been developed and applied to determine the best cutting procedure for wood stick frame houses. Restrictions, such as nominal lumber dimensions and sizes encountered in the North American market, were taken into account. A two-dimensional optimization for sheathing layout has also been developed to minimize the disposal of OSB boards, making the construction process leaner and more economical. A direct and positive impact on the environment and a reduction in construction costs are expected as a result of reducing the amount of primary materials utilized. [ABSTRACT FROM AUTHOR]

Abstract: A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted. [Copyright &y& Elsevier]

Abstract: In many research areas including medicine and paper coating, packing of particles together with numerical simulation is used for understanding important material functionalities such as optical and mass transfer properties. Computational packing of particles allows for analysing those problems not possible or difficult to approach experimentally, e.g., the influence of various shapes and size distributions of particles. In this paper a voxel-based algorithm by Jia et al. [X. Jia, R.A. Williams, A packing algorithm for particles of arbitrary shapes, Powder Technology 2001, vol. 120, pp. 175–186.] enabling the packing of arbitrarily shaped particles, is memory- and speed-optimised to allow for simulating significantly larger problems than before. Algorithmic optimisation is carried out using particle shell area reduction decreasing the amount of time spent on collision detection, fast rotation routines including lookup tables, and a bit packing algorithm to utilise memory effectively. Presently several hundreds of thousands of complex arbitrarily shaped particles can be simulated on a desktop machine in a simulation box consisting of more than 109 voxels. [Copyright &y& Elsevier]

Abstract: Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The “simultaneous” algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2–3fps). The “project-out” algorithm for fitting an AAM achieves faster than real time performance (>200fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the “simultaneous” AAM algorithm along with real time fitting speeds (35fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the “exhaustive local search” (ELS) algorithm. Experiments were conducted on the CMU MultiPIE database. [Copyright &y& Elsevier]

Fitting the measured bioimpedance spectroscopy (BIS) data to the Cole model and then extracting the Cole parameters is a common practice in BIS applications. The extracted Cole parameters then can be analysed as descriptors of tissue electrical properties. To have a better evaluation of physiological or pathological properties of biological tissue, accurate extraction of Cole parameters is of great importance. This paper proposes an improved Cole parameter extraction based on bacterial foraging optimization (BFO) algorithm. We employed simulated datasets to test the performance of the BFO fitting method regarding parameter extraction accuracy and noise sensitivity, and we compared the results with those of a least squares (LS) fitting method. The BFO method showed better robustness to the noise and higher accuracy in terms of extracted parameters. In addition, we applied our method to experimental data where bioimpedance measurements were obtained from forearm in three different positions of the arm. The goal of the experiment was to explore how robust Cole parameters are in classifying position of the arm for different people, and measured at different times. The extracted Cole parameters obtained by LS and BFO methods were applied to different classifiers. Two other evolutionary algorithms, GA and PSO were also used for comparison purpose. We showed that when the classifiers are fed with the extracted feature sets by BFO fitting method, higher accuracy is obtained both when applying on training data and test data. [ABSTRACT FROM AUTHOR]

Many automation or manufacturing systems are too complex to be modeled by analytical approaches and can only resort to fast-running simulation. Stochastic Nelder–Mead simplex method (SNM) is a newly developed methodology for simulation optimization with expected-value-based objective functions. Quantile, as an important alternative to the usual expected value, provides additional information about the distribution of system performance. In particular, it is useful in describing the tail behavior of the distribution. In this paper, we exploit the structure of SNM and extend it to solve simulation optimization problems with quantile-based objective functions and inequality constraints. The proposed method, called SNM-QC, utilizes the same search strategy as SNM but further incorporates effective quantile estimation techniques and penalty function approaches to solve the problem. We prove that SNM-QC has the desirable global convergence guarantee, i.e., the algorithm is guaranteed to converge to the true optima with probability one. One advantage of SNM-QC is that it is a direct search method that determines the moving direction simply by comparing a set of solutions rather than estimating gradient, thus it can handle many practical problems where gradient does not exist or is difficult to estimate. An extensive numerical study shows that the performance of SNM-QC is promising compared to the existing heuristics. Two illustrative applications are provided in the end to demonstrate the viability of SNM-QC in practical settings. [ABSTRACT FROM AUTHOR]

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion. [ABSTRACT FROM AUTHOR]

Despite the popularity of polygonization of implicit surfaces in graphics applications, an efficient solution to both polygonize and optimize meshes from implicit surfaces on modern GPUs has not been developed to date. In this paper, we introduce a practical GPU-based approach to efficiently polygonize and optimize iso-surface meshes for implicit surfaces. Specifically, we design new schemes to maximally exploit the parallel features of the GPU hardware, by optimizing both the geometry (vertex position, vertex distribution, triangle shape, and triangle normal) and the topology (connectivity) aspects of a mesh. Our experimental results show that, besides significant improvement on the resultant mesh quality, our GPU-based approach is approximately an order of magnitude faster than its CPU counterpart and faster than or comparable to other GPU iso-surface extraction methods. Furthermore, the achieved speedup becomes even higher if the resolution of the iso-surface is increased. [ABSTRACT FROM AUTHOR]

In a hard braking on a split-μ road, the achievement of shorter stopping distance while maintaining the vehicle in the straight line are of great importance. In this paper, to achieve these conflicting aims, an optimal nonlinear algorithm based on the prediction of vehicle responses is presented to distribute the wheel braking forces properly. The base of this algorithm is reducing the maximum achievable braking forces of one side wheels, as low as possible, so that the minimum stabilizing yaw moment is produced. The optimal property of the proposed control method makes it possible to get a trade-off between the shorter stopping distance and the less deviation of the vehicle heading from the straight line. The special case of this algorithm leads to the conventional anti-lock braking system (ABS) which generates the maximum braking forces for all wheels to attain the minimum stopping distance. However, the ABS cannot control the vehicle directional stability directly. The simulation results carried out using a nonlinear 8-DOF vehicle model demonstrate that the designed control system has a suitable performance to attain the desired purposes compared with the convectional ABS. [ABSTRACT FROM AUTHOR]

In this paper, a novel protection of coordinating optimization algorithm is proposed. Maximizing the penetration's dispersed generation and at the same time minimizing the fuse's operating time are the targets. A novel optimization technique, the Imperialistic Competition Algorithm (ICA), is applied to solve the problem. The results of simulations confirm that the proposed method leads to lower operating times of protective devices and higher possible DG penetration, compared with the traditional coordination techniques. [ABSTRACT FROM AUTHOR]

In this paper we deal with decision-coupled problems involving multiple agents over a network. Each agent has its own local objective function and local constraints, and all agents aim at finding the value of a common decision vector that minimizes the sum of all agents' cost functions and satisfies all local constraints. To this purpose, we introduce a Proximal-Tracking distributed optimization algorithm that integrates dynamic average consensus within the proximal minimization method. Convergence to an optimal consensus solution is guaranteed for any value of a constant penalty parameter, under a convexity assumption only, without requiring differentiability, Lipschitz continuity, or smoothness of the local objective functions. Numerical simulations show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, as an extension of a previous study, an improved approximation for the Gaussian Q-function is presented. The nonlinear least squares algorithm is employed to optimize the coefficients of the proposed approximation. The accuracy of the presented approximation is evaluated using extensive computer simulations. Results show that the proposed approximation has superior accuracy in high arguments' region when compared to the performance of other approaches introduced in the literature. [ABSTRACT FROM AUTHOR]

Automated manufacturing processes such as automotive tandem press lines include time dependent complex control functions. All motions and critical interactions between moving parts must be synchronised to avoid collisions and reach high production rate. It is even for a skilled operator hard to optimise these processes on-line. Therefore, a hardware-in-the-loop simulation including real industrial control systems and its control code establish an essential tool for optimisation. Additionally, an efficient optimisation algorithm is required to reach a useful simulation-based optimisation method. This paper proposes a new optimisation algorithm starting with the Lipschitzian algorithm DIRECT as global search and then switches over to the Nelder–Mead simplex algorithm for local convergence. During the switch over, the new algorithm determines all local candidates of the set of points evaluated by DIRECT and starts multiple Nelder–Mead local searches in each of these. An optimisation study for an automotive press line shows that the proposed algorithm combines the benefits of the Lipschitzian and the simplex algorithms in an efficient way. The importance of multiple local searches from all local candidates found is also shown in the study. Based on the same number of function evaluations, it is also shown that this algorithm reaches improved press line performances compared to the stochastic differential evolution algorithm. [ABSTRACT FROM AUTHOR]

This paper analyzes the effect of noise on several maximum power point tracking (MPPT) algorithms for photovoltaic systems. Noise is an essential consideration for optimization of MPPT algorithms. For example, for a perturb and observe algorithm, the perturbation size must be big enough to produce a signal larger than the measurement noise; however, the larger the perturbation, the farther the system will deviate from the maximum power point in steady state. There is also a tradeoff with respect to tracking speed, as slower tracking allows for more averaging of the signals, mitigating the noise. The effect of noise and other parameters on tracking performance is quantified, leading to an optimization of the system parameters to provide the best tracking accuracy for a specified tracking speed. The performance of the various algorithms is compared, and the analysis is verified by both simulations and experiments. [ABSTRACT FROM PUBLISHER]

Abstract: This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization. [Copyright &y& Elsevier]

There is a strong need for the optimized management of the thermal problem in Nd:YAG laser rod and for a powerful, fast, and accurate modelling tool capable of treating the heat source distribution very close to what it actually is. In this paper, a new optimization algorithm called bacterial foraging optimization algorithm (BFOA) is proposed for simulation of the radial heat distribution. A BFOA discloses a simulation method which delivers the exact temperature distribution in a circularly cylindrical structure with a circularly symmetrical, longitudinally, and transversally non-uniform heat source distribution and circularly symmetrical cooling means. The output power is obtained and compared with previously published experimental measurements for different pump power and a good agreement has been found. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: This paper analyses the problem of allocating beds among hospital wards in order to minimise crowding. Method: We present a generic discrete event simulation model of patient flow through the wards of a hospital. In the generic model, each ward can have separate probability distributions for arrival times and length of stay, which may be time dependent. Output of the model is a matrix, with statistics on the utilisation of different hypothetical numbers of beds for each ward. This matrix is fed into an allocation algorithm, which distributes the available beds among the wards in an optimal way. We define bed utilisation either in terms of how often it is in use (prevalence), or in terms of how often a newly arriving patient is placed in it (incidence). For these classes of utilisation measures we develop efficient allocation algorithms, which we prove to be optimal. Application: The model was applied to Akershus University Hospital in Norway. In 2011, some of the wards of this hospital experienced a high occupancy rate, while others had a lower utilisation. Our model was applied in order to reallocate the hospital beds among the wards. For each ward, acute arrivals were modelled with Poisson-distributions with time-varying intensity, while elective arrivals were programmed to arrive in specific numbers at specific times. The arrival rates were based on empirical data for 2010, scaled up by an expected increase of 40% due to a restructuring of the hospital districts in Oslo and the greater metropolitan area in 2011. Length of stay was modelled as beta-distributions, using a combination of subject matter experts’ evaluations and empirical data from 2010. The model has been verified and validated. Results: Intuitively, both prevalence (average number of crowding beds in use) and incidence (number of patients placed in crowding beds) might seem like relevant optimisation criteria. However, our experiments show that prevalence optimisation gives more sensible solutions than incidence optimisation, as the latter tends to sacrifice entire wards where length of stay is long and patient turnover is slow. Prevalence optimisation was therefore used. The main results show that when the bed distribution is optimised, the share of crowding patient nights is reduced from 6.5% to 4.2%. Conclusion: This model provides a powerful tool for optimising hospital bed utilisation, and the application showed an important reduction in crowding bed usage. The generic model is flexible, as the level of detail in the modelling of arrivals and length of stay can vary according to the data available and accuracy required. [Copyright &y& Elsevier]

Abstract: This paper presents an efficient single-stage Firefly-based algorithm (FA) to simultaneously optimise the size, shape and topology of truss structures. The optimisation problem uses the minimisation of structural weight as its objective function and imposes displacement, stress and kinematic stability constraints. Unstable and singular topologies are disregarded as possible solutions by checking the positive definiteness of the stiffness matrix. Because cross-sectional areas are usually defined by discrete values in practice due to manufacturing limitations, the optimisation algorithm must assess a mixed-variable optimisation problem that includes both discrete and continuous variables at the same time. The effectiveness of the FA at solving this type of optimisation problem is demonstrated with benchmark problems, the results for which are better than those reported in the literature and obtained with lower computational costs, emphasising the capabilities of the proposed methodology. In addition, the procedure is capable of providing multiple optima and near-optimal solutions in each run, providing a set of possible designs at the end of the optimisation process. [Copyright &y& Elsevier]

Abstract: This paper introduces an automatic feature extraction algorithm for bearing fault diagnosis using correlation filtering-based matching pursuit. This algorithm is described and investigated in theory and practice on both simulated and real bearing vibration signals. First, the vibration model for rolling bearing with fault is derived. Then, the numerical simulation signal being taken as an example, the principle of matching pursuit is mathematically explained and its drawbacks are analyzed. Afterward, to enhance the similarity of model related to the bearing faulty impulses, the model shape parameters are optimized using spectrum kurtosis and smoothing index. After that, the model with optimum shape and period parameters is taken as a template to approximate the impulses in faulty bearing signal. Finally, based on maximizing correlation principle, the optimized cycle parameter being as impuls e repetition period is matched up. The proposed method has been successfully applied in actual vibration signals of rolling element bearing with different faults. [Copyright &y& Elsevier]

Purpose: The purpose of this paper is to propose a heuristic for the inventory management of smart vending machine systems with product substitution under the replenishment point, order-up-to level policy and to evaluate its performance. Design/methodology/approach: The heuristic is developed on the basis of the decoupled approach. An integer linear mathematical model is built to determine the number of product storage compartments and replenishment threshold for each smart vending machine in the system and the Clarke and Wright's savings algorithm is applied to route vehicles for inventory replenishments of smart vending machines that share the same delivery days. Computational experiments are conducted on several small-size test problems to compare the proposed heuristic with the integrated optimization mathematical model with respect to system profit. Furthermore, a sensitivity analysis is carried out on a medium-size test problem to evaluate the effect of the customer service level on system profit using a computer simulation. Findings: The results show that the proposed heuristic yielded pretty good solutions with 5.7% error rate on average compared to the optimal solutions. The proposed heuristic took about 3 CPU minutes on average in the test problems being consisted of 10 five-product smart vending machines. It was confirmed that the system profit is significantly affected by the customer service level. Originality/value: The inventory management of smart vending machine systems is newly treated. Product substitutions are explicitly considered in the model. The proposed heuristic is effective as well as efficient. It can be easily modified for application to various retail vending settings under a vendor-managed inventory scheme with POS system. [ABSTRACT FROM AUTHOR]

Abstract: The paper studies the benefits of multi-path content delivery from a rate-distortion efficiency perspective. We develop an optimization framework for computing transmission schedules for streaming media packets over multiple network paths that maximize the end-to-end video quality, for the given bandwidth resources. We comprehensively address the two prospective scenarios of content delivery with packet path diversity. In the context of sender-driven systems, our framework enables the sender to compute at every transmission instance the mapping of packets to network paths that meets a rate constraint while minimizing the end-to-end distortion. In receiver-driven multi-path streaming, our framework enables the client to dynamically decide which packets, if any, to request for transmission and from which media servers, such that the end-to-end distortion is minimized for a given transmission rate constraint. Via simulation experiments, we carefully examine the performance of the scheduling framework in both multi-path delivery scenarios. We demonstrate that the optimization framework closely approaches the performance of an ideal streaming system working at channel capacity with an infinite play-out delay. We also show that the optimization leads to substantial gains in rate-distortion performance over a conventional content-agnostic scheduler. Through the concept of error-cost performance for streaming a single packet, we provide another useful insight into the operation of the optimization framework and the conventional scheduling system. [Copyright &y& Elsevier]

The harmony search (HS) method is a popular meta-heuristic optimization algorithm, which has been extensively employed to handle various engineering problems. However, it sometimes fails to offer a satisfactory convergence performance under certain circumstances. In this paper, we propose and study a hybrid HS approach, HS-PBIL, by merging the HS together with the population-based incremental learning (PBIL). Numerical simulations demonstrate that our HS-PBIL is well capable of outperforming the regular HS method in dealing with nonlinear function optimization and a practical wind generator optimization problem. [ABSTRACT FROM AUTHOR]

Abstract: In presence of obstacles, inter-agent pulling actions must be bounded. In this case, to remain connected to the group, the leader-agent (LA) must perform an active leading strategy. In this paper, an active leading algorithm is proposed which monitors the neighborhood of the LA and adjusts its velocity. The algorithm is based on the ant colony optimization (ACO) technique. As a real time optimization package, the ACO algorithm maximizes influence of the LA on the group, leading to fast flocking. Comparison with another optimization method is provided as well. Simulations show that the algorithm is successful and cost effective. [Copyright &y& Elsevier]

In this paper, an inversion algorithm has been proposed and validated, based on a fast simulator of pulsed eddy current testing (PECT) signals and a deterministic optimization strategy, for the profile reconstruction of wall thinning in pipes of nuclear power plants. Local wall thinning is modelled as a group of planar defects with different length and depth, reconstructed from two-dimensional PECT signals. Through inverse analysis of simulated signals and signals with random noise, the efficiency and the robustness of the proposed algorithm have been demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study the distributed containment control problem for networked Lagrangian systems with multiple dynamic leaders in the presence of parametric uncertainties under a directed graph that characterizes the interaction among the leaders and the followers. We propose a distributed adaptive control algorithm combined with distributed sliding-mode estimators. A necessary and sufficient condition on the directed graph is presented such that all followers converge to the dynamic convex hull spanned by the dynamic leaders asymptotically. As a byproduct, we show a necessary and sufficient condition on leaderless consensus for networked Lagrangian systems under a directed graph. Numerical simulation results are given to show the effectiveness of the proposed control algorithms. [Copyright &y& Elsevier]

Abstract: Much research on Artificial Intelligence (AI) has been focusing on exploring various potential applications of intelligent systems. In most cases, the researches attempt to model human intelligence by mimicking the brain structure and function, but they ignore an important aspect in human learning and decision making: the artificial emotion. In this paper, we present a new unconstrained global optimization method, hybrid chaos optimization algorithm with artificial emotion (HCOAAE), which avoids trapping to local minima, and improves convergence in large space and high-dimension optimization problems. The main purpose of artificial emotion is to mimic decision making behavior process of humans, to choose most suitable parameters of HCOAAE and decide whether to change current search strategy or not in the next iteration. Numerical simulations of 13 benchmark functions with different dimensions are used to test the performance of HCOAAE. Experimental results show that the proposed method significantly outperforms the existing methods in terms of convergence speed, computational effectiveness, and numerical stability. [Copyright &y& Elsevier]

Abstract: In this paper, we present an adaptive extremum seeking control scheme for fed-batch bioreactors with Haldane kinetics. The proposed adaptive extremum seeking approach utilizes the structure information of the process kinetics to derive a seeking algorithm that drives the system states to the desired setpoints that maximize the biomass production. It assumes that only the substrate concentration is available for on-line measurement. Lyapunov stability is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. The performance of the approach is illustrated via numerical simulations. [Copyright &y& Elsevier]

Electrical impedance tomography (EIT) is a non-invasive imaging modality which has been actively studied for its industrial as well as medical applications. However, the performance of the inverse algorithms to reconstruct the conductivity images using EIT is often sub-optimal. Several factors contribute to this poor performance, including high sensitivity of EIT to the measurement noise, the rounding-off errors, the inherent ill-posed nature of the problem and the convergence to a local minimum instead of the global minimum. Moreover, the performance of many of these inverse algorithms heavily relies on the selection of initial guess as well as the accurate calculation of a gradient matrix. Considering these facts, the need for an efficient optimization algorithm to reach the correct solution cannot be overstated. This paper presents an oppositional biogeography-based optimization (OBBO) algorithm to estimate the shape, size and location of organ boundaries in a human thorax using 2D EIT. The organ boundaries are expressed as coefficients of truncated Fourier series, while the conductivities of the tissues inside the thorax region are assumed to be known a priori. The proposed method is tested with the use of a realistic chest-shaped mesh structure. The robustness of the algorithm has been verified, first through repetitive numerical simulations by adding randomly generated measurement noise to the simulated voltage data, and then with the help of an experimental setup resembling the human chest. An extensive statistical analysis of the estimated parameters using OBBO and its comparison with the traditional modified Newton-Raphson (mNR) method are presented. The results demonstrate that OBBO has significantly better estimation performance compared to mNR. Furthermore, it has been found that OBBO is robust to the initial guess of the size and location of the boundaries as well as offering a reasonable solution when the a priori knowledge of the conductivity of the organs is not very accurate. [ABSTRACT FROM AUTHOR]