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This paper investigates the distributed optimization problem of networked Euler–Lagrange (EL) systems with actuator faults and unknown model parameters. In order to solve this issue, some event‐triggered fault‐tolerant (ETFT) optimal coordination algorithms with the mechanism of intermittent communication and intermittent controller update are designed for the first time to compensate for the impact of physical faults on the system, and reduce communication costs and control resource consumption. For practical applications, it is difficult for the system to have a secure environment if there exist malicious attacks blocking network channels among agents. Therefore, the resilient ETFT optimal coordination with intermittent communication subject to asynchronous denial‐of‐service (DoS) attacks is considered. Especially, auxiliary systems with adaptive gains are introduced in the algorithms due to the unavailability of the EL model parameters, which also enable virtual data exchange between agents and protect actual state information. Note that the algorithms are fully distributed as the global topology information is not required. Finally, the feasibility of the proposed algorithms is verified through numerical simulation of examples. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

Antenna array synthesis is quite a popular topic in electromagnetics and finding suitable array parameter values is a challenging nonlinear engineering problem in this field. In recent years, it has been realized that optimization algorithms can be used to achieve desired solutions in this area. Besides, improvements in the optimization algorithms make it possible to perform synthesis processes on different array structures and to obtain better results. In this paper, a method based on a combination of ant lion optimizer (ALO) and sequential quadratic programming (SQP) is proposed for the concentric circular antenna array (CCAA) synthesis. Excitation amplitudes of array elements are optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). The results of proposed ALO‐SQP method are compared with those of 11 different algorithms: ALO, moth flame optimizer (MFO), SQP, symbiotic organisms search (SOS), biogeography based optimization (BBO), opposition‐based gravitational search algorithm (OGSA), cat swarm optimization (CSO), firefly algorithm (FA), evolutionary programming (EP), backtracking search optimization algorithm (BSA), and seeker optimization algorithm (SOA). The comparisons indicate that the CCAAs synthesis using the proposed method presents good MSLs, FNBWs, and DRRs. [ABSTRACT FROM AUTHOR]

This paper introduces the electromagnetic clustering algorithm (ELMC), an enhanced variant of electromagnetic field optimization (EFO), for clustering. The motivation behind ELMC is to overcome the shortcomings of traditional k‐means clustering algorithm. The performance of k‐means primarily depends upon the initial choice of centroids, which can lead the algorithm towards an undesirable local optimum, if chosen incorrectly or inefficiently. The ELMC utilizes the attraction–repulsion concept of the EFO algorithm to maintain the diversity of the population, making it less vulnerable towards the initial choice of centroids. The performance of ELMC is validated on a set of benchmark problems, and the results are compared with other state‐of‐the‐art algorithms. Numerical and graphical results indicate the competence of the proposed ELMC algorithm. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a new detection mechanism for defending the cyberspace with a new logic that aiding the concept of deep learning. The process involves two phases, namely, feature extraction and classification. The initial phase is the feature extraction, in which the features are extracted from the given input data by the renowned principal component analysis (PCA). Subsequently, the extracted features are subjected to the classification phase, where the deep belief network (DBN) model is used. The DBN model classifies the presence of attacks like denial of service (DoS), probe, R2L, and U2R. In order to make the performance more excellent, this paper diverts the strategy to a new concept termed "Optimization Concept." Here, the hidden neuron of DBN is optimally selected by a new algorithm termed novel mutation rate‐based lion algorithm (NMR‐LA), which is the modified model of lion algorithm (LA). The performance of proposed algorithm NMR‐LA is compared over the conventional models in terms of both positive and negative measures like accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1 score and Mathews correlation coefficient (MCC), false‐positive rate (FPR), false‐negative rate (FNR), and false‐discovery rate (FDR) and proves the betterments of proposed work. [ABSTRACT FROM AUTHOR]

As distribution networks operate in their majority in a radial way, this condition becomes one of the most important constraints of distribution network reconfiguration (DNR) problem. There are diverse ways to represent this in DNR, but a lack of studies that provide an analysis of its behavior is noted, specially regarding its influence in metaheuristics and bio‐inspired metaheuristics. This paper presents a discussion between two alternatives to represent this in DNR in conjunction with three new bio‐inspired metaheuristics. One is based on the formation of the incidence matrix of the system and its determinant; the other is a modified approach of a method that establishes a set of forbidden switches through the analysis of the system fundamental loops. To solve DNR, three new bio‐inspired metaheuristics are presented: monarch butterfly optimization (MBO), gray wolf optimizer (GWO), and marine predators algorithm (MPA). A comparison is performed with selective particle swarm optimization (SPSO). To test the alternatives, three systems are used (33‐, 69‐, and 84‐ bus), with results showing the influence of the radiality approach in the results of bio‐inspired metaheuristics. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSN) encompasses millions of nodes that work jointly, senses environmental information, and transmits to the Base Station (BS). "Energy hole" or "hot‐spot" is a problem in which sensor nodes near to the BS deceasetoo early which creates the partitioning of the network. This is due to the reason of the difference in the consumption of energy by the nodes in WSN which reduces the lifespan of the sensor network. The distribution of the cluster heads and the load balancing are the major pitfalls. In this paper to deal with the statedpitfalls, a hybrid of fuzzy logic technique and harmony search algorithm is proposed to lengthen the lifetime of the network. Unequal clustering is a technique proposed by the researchers to handle the hot‐spot issue, hence it is also included. The proposed algorithm forms unequal clusters. To prove the efficiency of the proposed algorithm, some well‐known unequal clustering algorithms and harmony search based algorithmare used to compare the proposed algorithm under different network settings. In all the settings, better performance is achieved by the proposed protocol. It prolongs the lifespan of the network as compared to the different algorithms proposed so far. [ABSTRACT FROM AUTHOR]

The survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either benign (B) or malignant (M) using an improved crow‐search optimization algorithm (ImCSOA). In the literature, the CSOA is generally used for solving several feature selection and numerical optimization problems. The objective is to utilize this popular optimization algorithm for the problem of biomedical image classification. However, if this algorithm is applied directly to classification problems, then it will result in poor classification of data. Hence, the original CSO (OCSO) algorithm undergoes suitable enhancements using a novel controlled parameter tuning, control operator and chaotic‐maps‐based controlled randomness. Four distinct chaotic maps are used for controlling the randomness in the OCSO algorithm. The mammogram images are obtained from the Mammographic Image Analysis Society and Digital Database for Screening Mammography data sets for the evaluation. The classification is accomplished through discrete wavelet transform‐based statistical features that are extracted at two levels [level 4 (L4) and level 6 (L6)] of decomposition. For both data sets, the ImCSOA with L4 and L6 decomposed bior4.4 wavelet features provides the maximum accuracy of around 85% to 86%, which is approximately 62% to 88% better than the OCSO algorithm with L4 and L6 decomposed bior4.4 wavelet features. [ABSTRACT FROM AUTHOR]

Summary: The novel and optimal phasor measurement unit (PMU) observability has been attained based on the flower pollination algorithm (FPA). The prime objective is to select the optimal bus for placing the PMU to maximize the number of bus observance in the system. Many optimization techniques have been developed with an aim to optimize the PMU's required quantity with attaining full observability of the network. This paper proposes a new technique of modified FPA, which reduces the computation time and achieves greater accuracy. This approach also helps in achieving the maximum observability of power system based on choosing the optimum buses to place the PMUs. The proposed algorithm would be best suited and offers highly accurate results when analyzing the system for maximum observability with the minimum number of allotted PMUs. The FPA algorithm is implemented on standard IEEE 14 bus, IEEE 30 bus, and IEEE 118 bus systems. The proposed FPA algorithm outperforms other evolutionary algorithms. It yields better overall performance than other algorithm methods, in terms of the minimum number of PMUs with maximum observability, including contingency analysis. [ABSTRACT FROM AUTHOR]

Summary: A fractional delay filter is used to increase the accuracy, preciseness, time synchronization, and stability of signal processing system. However, designing a fractional delay filter for a specified delay, without affecting spectral characteristics of the signal is challenging because of nondifferentiability and multimodal nature of its objective function. In this paper, a more accurate design technique has been proposed for designing fractional delay filters, based on a recently developed firefly algorithm and its improved version. The designed filters offer variable fractional delay. A novel symmetric structure of implementation has been used to design filters. The efficacy of the proposed technique is evaluated by considering a filter design example. The performance of the proposed technique is compared with the other exiting algorithm. The comparative analysis of finite impulse response (FIR) fractional delay filter design proves that the proposed algorithm has a smaller design error and an implementation complexity than the other reported existing algorithms. In addition to this, the designed FIR fractional delay filter is implemented on Xilinx Virtex‐7 for experimental validation. [ABSTRACT FROM AUTHOR]

Summary: The technical growth in the field of the wireless sensor networks (WSNs) has resulted in the process of collecting and forwarding the massive data between the nodes, which was a major challenge to the WSNs as it is associated with greater energy loss and delay. This resulted in the establishment of a routing protocol for the optimal selection of the multipath to progress the routing in WSNs. This paper proposes an energy‐efficient routing in WSNs using the hybrid optimization algorithm, cat–salp swarm algorithm (C‐SSA), which chooses the optimal hops in progressing the routing. Initially, the cluster heads (CHs) are selected using the low‐energy adaptive clustering hierarchy (LEACH) protocol that minimizes the traffic in the network. The CHs are engaged in the multihop routing, and the selection of the optimal paths is based on the proposed hybrid optimization, which chooses the optimal hops based on the energy constraints, such as energy, delay, intercluster distance, intracluster distance, link lifetime, delay, and distance. The simulation results prove that the proposed routing protocol acquired minimal delay of 0.3165 with 50 nodes and two hops, maximal energy of 0.1521 with 50 nodes and three hops, maximal number of the alive nodes as 39 with 100 nodes and two hops, and average throughput of 0.9379 with 100 nodes and three hops. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is a very powerful analytical technique with an increasing acceptance in the scientific community. For the optimization of the Raman acquisition, two main parameters, the integration time and the number of accumulations, need to be adjusted to the sample under analysis, as the sample, or even different spots on the same sample, can provide very different Raman responses one from another. In this paper, we present a suite of algorithms to automate the acquisition parameter adjustment to the sample under analysis, addressing issues such as spectral saturation, fluorescence, cosmic ray detection and removal, and adjustment of the acquisition parameters to optimize the acquired spectral data. This suite has been developed in the framework of the Raman Laser Spectrometer (RLS) instrument development for the Exomars mission but can be applied to any Raman spectrometer. This will allow the spectrometer to adapt to the characteristics of the sample that is being analyzed, optimizing the total operative time, while improving the usability and overall efficiency of the system. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We compare some well-known Bayesian global optimization methods in four distinct regimes, corresponding to high and low levels of measurement noise and to high and low levels of 'quenched noise' (which term we use to describe the roughness of the function we are trying to optimize). We isolate the two stages of this optimization in terms of a 'regressor,' which fits a model to the data measured so far, and a 'selector,' which identifies the next point to be measured. The focus of this paper is to investigate the choice of selector when the regressor is well matched to the data. [ABSTRACT FROM AUTHOR]

SUMMARY Based on recent papers that have demonstrated that robust iterative learning control can be based on parameter optimization using either the inverse plant or gradient concepts, this paper presents a unification of these ideas for discrete-time systems that not only retains the convergence properties and the robustness properties derived in previous papers but also permits the inclusion of filters in the input update formula and a detailed analysis of the effect of non-minimum-phase dynamics on algorithm performance in terms of a 'plateauing' or 'flat-lining' effect in the error norm evolution. Although the analysis is in the time domain, the robustness conditions are expressed as frequency domain inequalities. The special case of a version of the inverse algorithm that can be used to construct a robust stable anti-causal inverse non-minimum-phase plant is presented and analysed in detail. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper introduces a new iterative algorithm for the estimation of the mixture transition distribution (MTD) model, which does not require the use of any specific external optimization procedure and can therefore be programmed in any computing language. Comparisons with previously published results show that this new algorithm performs at least as well as or better than other methods. The choice of initial values is also discussed. The MTD model was designed for the modeling of high-order Markov chains and has already proved to be a useful tool for the analysis of different types of time series such as wind speeds and social relationships. In this paper, we also propose to use it for the modeling of one-dimensional spatial data. An application using a DNA sequence shows that this approach can lead to better results than the classical Potts model. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm that optimizes the ventricular fiber structure of the human heart. A number of histological studies and diffusion tensor magnetic resonance imaging analyses have revealed that the myocardial fiber forms a right-handed helix at the endocardium. However, the fiber formation changes its orientation as a function of transmural depth, becoming a left-handed helix at the epicardium. To determine how nature can construct such a structure, which obtains surprising pumping performance, we introduce macroscopic modeling of the branching structure of cardiac myocytes in our finite element ventricular model and utilize this in an optimization process. We put a set of multidirectional fibers around a central fiber orientation at each point of the ventricle walls and simulate heartbeats by generating contraction forces along each of these directions. We examine two optimization processes using the workloads or impulses measured in these directions to update the central fiber orientation. Both processes improve the pumping performance towards an optimal value within several tens of heartbeats, starting from an almost-flat fiber orientation. However, compared with the workload optimization, the impulse optimization produces better agreement with experimental studies on transmural changes of fiber helix angle, streamline patterns of characteristic helical structures, and temporal changes in strain. Furthermore, the impulse optimization is robust under geometrical changes of the heart and tends to homogenize various mechanical factors such as the stretch and stretch rate along the fiber orientation, the contraction force, and energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents a design method of data-dependent filters that uses simplified fuzzy interference. Since the antecedents of fuzzy inference can comprise several local characteristics (i.e., observation values), it is possible for the fuzzy filter to adjust its weights to adapt to local image data. The tuning of membership functions and fuzzy rules of the proposed filter results in a least mean square (LMS)-like algorithm. Thus, local characteristics can be increased for the proposed fuzzy filter optimally. This paper, introduces a new observation value (calculated from local statistics) into the proposed filter. The proposed filter changes filter behavior according to the local properties of signals and provides good noise attenuation in all regions of image, including detail regions while still preserving the details. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, liquid-liquid equilibrium in decanter of heterogeneous azeotropic distillation is solved by minimizing Gibbs free energy, by using global optimization technique. The azeotropic distillation column is modeled by solving material balance, equilibrium and summation equations, and repulsive particle swarm optimization (RPSO), a stochastic global optimization formalism, is employed to predict stable steady state solution in decanter. To verify the performance of the RPSO algorithm, it is compared with equation solving method for liquid-liquid equilibria by considering two azeotropic systems, namely (1) benzene-ethanol-water and (2) furfural-water. The proposed methodology shows feasibility of the RPSO algorithm in predicting liquid-liquid equilibrium in decanter of heterogeneous azeotropic distillation. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has lately become very popular in the electromagnetics domain. Although in some instances PSO shows a superior performance compared with other global optimization techniques, it is still computationally more expensive relative to classical gradient techniques. In this paper, a cooperative particle swarm optimization (CPSO) is adopted to achieve a faster convergence compared with the conventional PSO, while maintaining its main feature, which is the capability of finding global optimum. In order to deploy PSO more efficiently, the often neglected effect of the initial population on the overall convergence of PSO is discussed. It is shown that subdividing the space into grid cells and using random distribution within these cells will give the best results in terms of convergence speed. Different boundary conditions are tried on the CPSO algorithm. In order to verify the performance of the proposed algorithm, the algorithm is compared with the conventional PSO using six different objective functions. As a design example, an ultra-wide-band filter is designed. The results show a slightly faster convergence compared with the conventional PSO. The designed filter is fabricated and experimental results are also shown. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Estimation of a sparse signal representation, one with the minimum number of nonzero components, is hard. In this paper, we show that for a nontrivial set of the input data the corresponding optimization problem is equivalent to and can be solved by an algorithm devised for a simpler optimization problem. The simpler optimization problem corresponds to estimation of signals under a low-spread constraint. The goal of the two optimization problems is to minimize the Euclidian norm of the linear approximation error with an lp penalty on the coefficients, for p = 0 (sparse) and p = 1 (low-spread), respectively. The l0 problem is hard, whereas the l1 problem can be solved efficiently by an iterative algorithm. Here we precisely define the l0 optimization problem, construct an associated l1 optimization problem, and show that for a set with open interior of the input data the optimizers of the two optimization problems have the same support. The associated l1 optimization problem is used to find the support of the l0 optimizer. Once the support of the l0 problem is known, the actual solution is easily found by solving a linear system of equations. However, we point out our approach does not solve the harder optimization problem for all input data and thus may fail to produce the optimal solution in some cases. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 10–17, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20034 [ABSTRACT FROM AUTHOR]

This paper presents the design of a wideband corrugated multisectional conical horn using a genetic algorithm (GA). The GA optimizes the length, flare angle, and first slot depth of each section of the horn in order to match the admittance, reduce the voltage standing wave ratio (VSWR) of the horn, and generate the desired pattern. A VSWR of less than 1.6 is obtained over the frequency range 11–18 GHz. The optimized multisectional conical horn antenna has four sections, is less than 7λ long, and has very low sidelobe levels. The ratio between ratio between the co-polarization and the cross-polarization patterns is greater than 60 dB. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 38: 352–356, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.11058 [ABSTRACT FROM AUTHOR]

The paper discusses the generation of a family of neural models for spiral inductors and MIM capacitors, and their use in broadband nonlinear circuit optimization by evolutionary algorithms. It is shown that the resulting optimization process is fast and well behaved, and can achieve the same circuit performance produced by a design approach based on lumped passive components. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 38: 46–52, 2003 [ABSTRACT FROM AUTHOR]

In this paper, the synthesis of a stacked patch antenna for circular polarization is presented. The antenna is analyzed by the full-wave approach based on the method of moments in the spectral domain. In order to find the optimal antenna parameters, a hybrid genetic algorithm that combines a global search of the common genetic algorithm and the efficiency of the direct search method is developed. The experimental results show SWR<2, with an axial ratio lower than 4 dB within 14% bandwidth. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 31: 197–201, 2001. [ABSTRACT FROM AUTHOR]

This paper proposes the two-dimensional (2-D) cutting-stock algorithm based on the local optimization. First, it is intended to eliminate the combination process for the rectangular parts in the traditional system. The search operation is defined as the one-to-one mapping of the rectangular parts to the partial rectangular regions generated by the divide operation. A stack is used to store the data for the partial rectangular regions, and the merge operation is introduced as a new concept of operation in the stack. The algorithm is constructed recursively using the following four operations. i.e., search, divide, merge, and stack. Through an application example, it is verified that the proposed algorithm can realize nearly the same layout efficiency as the traditional method by the method dynamic programming. [ABSTRACT FROM AUTHOR]

X-ray film-screen mammography is currently the gold standard for detecting breast cancer. However, one disadvantage is that it projects a three-dimensional (3D) object onto a two-dimensional (2D) image, reducing contrast between small lesions and layers of normal tissue. Another limitation is its reduced sensitivity in women with mammographically dense breasts. Computed tomography (CT) produces a 3D image yet has had a limited role in mammography due to its relatively high dose, low resolution, and low contrast. As a first step towards implementing quantitative 3D mammography, which may improve the ability to detect and specify breast tumors, we have developed an analytical technique that can use Compton scatter to obtain 3D information of an object from a single projection. Imaging material with a pencil beam of polychromatic x rays produces a characteristic scattered photon spectrum at each point on the detector plane. A comparable distribution may be calculated using a known incident x-ray energy spectrum, beam shape, and an initial estimate of the object’s 3D mass attenuation and electron density. Our iterative minimization algorithm changes the initially arbitrary electron density voxel matrix to reduce regular differences between the analytically predicted and experimentally measured spectra at each point on the detector plane. The simulated electron density converges to that of the object as the differences are minimized. The reconstruction algorithm has been validated using simulated data produced by the EGSnrc Monte Carlo code system. We applied the imaging algorithm to a cylindrically symmetric breast tissue phantom containing multiple inhomogeneities. A preliminary ROC analysis scores greater than 0.96, which indicate that under the described simplifying conditions, this approach shows promise in identifying and localizing inhomogeneities which simulate 0.5 mm calcifications with an image voxel resolution of 0.25 cm and at a dose comparable to mammography. [ABSTRACT FROM AUTHOR]