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Summary: Multi‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results. [ABSTRACT FROM AUTHOR]

Summary: Distributed optimization algorithms have various advantages and wide applications, while the distributed nature also makes them flimsy in the face of external attacks. In this paper two Byzantine attack models are designed, in which the edge attack model is seldom considered before. A resilient distributed consensus algorithm based on the trusted nodes and edges is proposed to defend against the two attack models. The agreement and optimality of the proposed consensus algorithm are guaranteed with theoretical analysis. The simulation results and comparison experiments verify the proposed algorithm to be more effective than the compared one. In the perspective of attackers, which attack model is more recommended is also analysed based on the experiments. [ABSTRACT FROM AUTHOR]

One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]

Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR]

Summary: The optimization of indoor femtocell base stations (FBS) placement is a highly complex problem in which multiple performance figures can be considered. In this paper, we propose a biobjective optimization approach based on non‐dominated sorting genetic algorithm II (NSGA‐II) to solve the indoor FBS deployment problem in residential scenarios where a single cell is deployed within the coverage of a single dominant interfering external macro base station (MBS). The optimization problem is formulated with the objective of jointly minimizing outage probability and maximizing normalized downlink average throughput. Several conditions are simultaneously considered under the framework of Monte Carlo simulations, such as the effect of co‐channel interference in uplink and downlink, buildings with arbitrary layouts, realistic transmission schemes and antenna radiation patterns, random user distributions, and a well‐established empirical path loss model. The proposed algorithm, which determines the location and orientation of the FBS's antenna that provides a suitable compromise between the two objective functions, is evaluated in this paper. First, two different experiments are proposed to investigate the algorithm convergence; then, statistical analyses are formulated to characterize the relation between the values achieved for both objective functions; finally, the computational load is evaluated. Results indicate the convergence of the proposed method towards the real Pareto front and a significant reduction of the computational time with respect to a reference method based on exhaustive search, while pointing at new criteria for the selection of the objective functions. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is one of the efficient recent meta‐heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA‐VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA‐VNS is able to provide very competitive results and outperforms the other algorithms. [ABSTRACT FROM AUTHOR]

Summary: The increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine‐tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi‐Objective Gradient‐based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device‐to‐device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC‐FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities. [ABSTRACT FROM AUTHOR]

To improve the state‐of‐the‐art works in breast cancer diagnosis, this paper proposes ensemble‐based deep‐transfer learning with classifiers namely optimizable K‐nearest neighbors, optimizable naïve Bayes, optimizable ensemble, and optimizable support vector machine algorithms for the auto‐feature extraction, detection, and severity classification of input mammograms. The hyperparameters of these classification algorithms are optimized by using the Bayesian optimization technique. The extracted robust features are normalized and then classified using the Bayesian optimized classifiers. Thus, the work throws a light of research on detecting whether the input mammogram is normal or abnormal. Afterward, it further focuses on the severity classification of abnormalities that is, benign or malignant. The aforesaid algorithms are trained and tested for the three‐class classification problem, thus achieving a maximum performance using the Bayesian optimized SVM algorithm applied with ResNet18 deep features providing classification accuracy of 99.689% for MIAS and 98.883% for INbreast dataset. [ABSTRACT FROM AUTHOR]

Stochastic gradient descent algorithm is a classical and useful method for stochastic optimisation. While stochastic gradient descent has been theoretically investigated for decades and successfully applied in machine learning such as training of deep neural networks, it essentially relies on obtaining the unbiased estimates of gradients/subgradients of the objective functions. In this paper, by constructing the randomised differences of the objective function, a gradient‐free algorithm, named the stochastic randomised‐difference descent algorithm, is proposed for stochastic convex optimisation. Under the strongly convex assumption of the objective function, it is proved that the estimates generated from stochastic randomised‐difference descent converge to the optimal value with probability one, and the convergence rates of both the mean square error of estimates and the regret functions are established. Finally, some numerical examples are prsented. [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]

Fourier ptychography is a new type of computational imaging technology, which uses a stack of low‐resolution images obtained from overlapped apertures (or equivalent) to reconstruct super‐resolved image. However, the accuracy of the aperture position will directly affect the quality and resolution of the reconstructed image. This paper proposes a new perspective for FP positional deviations correction using the neural network. We construct a trainable neural network to perform positional deviations correction along with object reconstruction. The real part and imaginary part of the object as well as the different and irregular positional deviations of each aperture are set as the weights of convolutional layer. The gradients over these weights are computed automatically, and gradient‐based optimization algorithm is employed to recover the object and find the correct aperture position. Simulation and experiment are performed to verify our algorithm. The results show that the proposed algorithm can accurately find the aperture position and improve the reconstruction quality. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed. [ABSTRACT FROM AUTHOR]

For modern complex system, quality has become a serious and difficult problem that restricts the generation of combat effectiveness. Withal, based on domestic and foreign research and analysis, this paper puts forward a trade‐off method for complex system reliability, maintainability, supportability, testability (RMST) design, which is suitable for product design stage. First, aiming at the uncertain problem of complex system constrained optimization, the good‐point set population initialization, Deb feasibility comparison method and ε constraint method are integrated into the flower pollination algorithm, to construct the algorithm for RMST quotas design and trade‐off. Second, taking the typical series‐parallel repairable system as an example, one general model for complex system RMST quotas design and trade‐off based on effectiveness is constructed. Finally, take an unmanned nuclear radiation detection system as a typical case, to verify its feasibility, advantages, and disadvantages. [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: Vehicular Ad hoc NETwork (VANET) facilitates ubiquitous connectivity for establishing Vehicle‐to‐Vehicle (V2V) communication and supporting Intelligent Transportation Systems (ITSs). This vehicle communication requires complete coverage within the target range for ensuring reliable message dissemination. High density of vehicles in the intersections introduces challenges due to obstacles such as buildings, foliage, and other moving vehicles, preventing exchange of information about location and message update between vehicles. Non‐Line‐of‐Sight (NLOS) nodes also introduce broadcasting storm problem leading to congestion that prevents emergency messages from reaching the target vehicular nodes. Integration of meta‐heuristic Crow Search Algorithm (CSA) and Gray Wolf Optimization Algorithm (GWOA) minimizes the objective function of NLOS localization problem without the solution being trapped into local optima. In this paper, a Hybrid Crow Search and GWOA‐based NLOS Positioning Scheme (HCSGWOA‐NLOS‐PS) is proposed to handle the issue of broadcast storm and facilitate reliability in emergency message delivery. The proposed HCSGWOA‐NLOS‐PS utilizes the benefits of Time of Arrival (ToA) and geographical information‐based cooperative localization agent for attaining efficient NLOS node positioning in the network. It uses the benefits of CSA and GWOA for positioning the NLOS nodes based on the intelligence derived from the crows' conduit and the social attacking behavior of gray wolves that are ideal for balancing the tradeoff between exploration and exploitation. The simulation results of the proposed HCSGWOA‐NLOS‐PS confirm a mean emergency message delivery of 11.82%, neighborhood awareness of 12.38% with reduced localization error rate of 2.36% and minimized delay of 8.64% when compared to the baseline approaches. [ABSTRACT FROM AUTHOR]

Summary: Vehicular Ad hoc NETwork (VANET) is considered as the appropriate candidate for provisioning risk‐free environment that confirms secure cooperation and very minimal congestion among the vehicular nodes in the network. The establishment and maintenance of connectivity between vehicular nodes are determined to be influenced by the existence of Non‐Line‐of‐Sight (NLOS) nodes that introduce channel congestion and broadcasting storm into the network during emergency message delivery. Thus, NLOS nodes need to be localized with optimality for enhancing the emergency data delivery rate with minimized latency degree and energy consumption in the network. In this paper, Harris Hawk Optimization Algorithm (HHOA)‐based NLOS nodes Localization Scheme (NLOS‐LS) (HHOA‐NLOS‐LS) is proposed for facilitating reliable data dissemination among vehicular nodes under emergency situations. HHOA utilizes chasing styles and cooperative behavior of Harris hawks termed as surprise pounce for efficient localization based on reference nodes. In particular, the intelligent strategy of Harris hawks' behavior in attacking the prey in all directions is included for localizing the NLOS nodes from the reference nodes positioned in all directions of the network. It is capable of localizing the NLOS nodes based on adaptive localizing (chasing) styles attained through reference nodes dependingon the dynamic nature of NLOS nodes. The simulation results prove that the mean localization rate is improved by 23.21%, mean neighborhood awareness rate by 19.82%, mean emergency message delivery rate by 18.32% and mean channel utilization by 17.28% when compared to the baseline Weighted Inertia‐based Dynamic Virtual Bat Algorithm (WIDVBA)‐based NLOS‐LS (WIDVBA‐NLOS‐LS), Cooperative Volunteer Vehicular Nodes (CVVN)‐based NLOS‐LS (CVVN‐NLOS‐LS), Vote Selection Mechanisms and Probabilistic Data Association (VSMPDA)‐based NLOS‐LS (VSMPDA‐NLOS‐LS), and Weighted Distance Hyperbolic Prediction (WDHP)‐based NLOS‐LS (WDHP‐NLOS‐LS) for a varying number of vehicular nodes in the network. [ABSTRACT FROM AUTHOR]

This study, describes the design problem of a thinned multiple concentric elliptical arrays of uniformly excited isotropic antennas using meta‐heuristic optimization algorithms; Teaching Learning Based Optimization (TLBO), Crow Search Algorithm (CSA) and Moth Flame Optimization (MFO) algorithm. Two simulations are performed here. In simulation 1, the synthesis of the thinned array structure is presented in the vertical plane at a scanning angle 30° and in simulation 2, the synthesis is performed in the horizontal plane. In both cases, our objective is to minimize side lobe level (SLL) to the extent possible and at the same time maximize the peak directivity to a level higher than the desired value. Here, all the optimization algorithms are applied to obtain the optimum number of switched on and off elements in every concentric ellipse that will meet the above objective. A comparative analysis of all three algorithms is performed using peak directivity, SLL and first null beam width (FNBW) as well as statistical parameters such as mean, SD, best cost function value and run time. To assess the enhanced outcome of the proposed optimization design comparison is also made with the results of a fully populated concentric elliptical array antenna (CEAA) for both the cases separately. This paper shows a measure of effectiveness and potential of all the algorithms mentioned above, for designing a thinned concentric elliptical array. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have an important role in establishing the communication between the Internet of things (IoT) devices. Routing is an important research field in the WSN as it helps in finding the suitable paths for the communication. This paper proposes a multipath routing algorithm based on the optimization approach. The proposed routing algorithm has two important phases, namely, Cluster head selection and multipath routing. Initially, the cluster head selection is carried out by the kernel based Fuzzy C Means (kernel FCM) algorithm. Then, the multipath routing is established by the newly developed Rider Salp Swarm Optimization algorithm (RSSA), which is the integration of the Rider Optimization Algorithm (ROA) and Salp Swarm Algorithm (SSA). The fitness function of the proposed RSSA is designed by considering the several factors, such as energy, QoS and trust. The simulation of the proposed work is carried out using different WSN setups, and the results are compared with several comparative techniques. From the results, it can be summarized that the proposed RSSA based multipath routing has better performance with values of 0.2526, 0.0764, 0.5, and 26 for delay, energy, throughput and number of alive nodes, respectively. [ABSTRACT FROM AUTHOR]

Summary: The reliability of data dissemination in vehicular ad hoc network (VANET) necessitates maximized cooperation between the vehicular nodes and the least degree of congestion. However, non‐line of sight (NLOS) nodes prevent the establishment and sustenance of connectivity between the vehicular nodes. In this paper, a hybrid seagull and thermal exchange optimization (TEO) algorithm‐based NLOS node detection technique is proposed for enhancing cooperative data dissemination in VANETs. It inherits three different versions of the proposed hybridized algorithm; three different approaches for localization of NLOS nodes depending upon its distance from the reference nodes are incorporated. It is considered as a reliable attempt in effective NLOS node localization as it is predominant in maintaining the balancing the degree of exploration and exploitation in the search process. In the first variant, the method of the roulette wheel is utilized for selecting one among the two optimization algorithm. In the second adoption, this hybridization algorithm combines TEO algorithm only after the iteration of SEOA algorithm. In the final adoption, the predominance of the seagull attack mode is enhanced by including the heat exchange formula of TEO algorithms for improving exploitation capability. The simulation experiments of the proposed HS‐TEO‐NLOS‐ND scheme conducted using EstiNet 8.1 exhibited its reliability in improving the emergency message delivery rate by 14.86%, a neighborhood awareness rate by 13%, and the channel utilization rate by 11.24%, compared to the benchmarked techniques under the evaluation done with different number of vehicular nodes and NLOS nodes in the network. [ABSTRACT FROM AUTHOR]

Summary: Task scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade‐off between the load, resource utilization, makespan, and Quality of Service (QoS). To bring a balance in the trade‐off, this paper proposes a method, termed as crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing (CPO‐MTS). The proposed algorithm decides the optimal execution of the available tasks in the available cloud resources in minimal time. The proposed algorithm is the fusion of the Crow Search optimization Algorithm (CSA) and the Penguin Search Optimization Algorithm (PeSOA), and the optimal allocation of the tasks depends on the newly designed optimization algorithm. The proposed algorithm exhibits a better convergence rate and converges to the global optimal solution rather than the local optima. The formulation of the multiobjectives aims at a maximum value through attaining the maximum QoS and resource utilization and minimum load and makespan, respectively. The experimentation is performed using three setups, and the analysis proves that the method attained a better QoS, makespan, Resource Utilization Cost (RUC), and load at a rate of 0.4729, 0.0432, 0.0394, and 0.0298, respectively. [ABSTRACT FROM AUTHOR]

The objective of this research paper is to categorize the magnetic resonance imaging (MRI) images as demented (DEM) or nondemented (ND) using improved chicken swarm optimization technique (ICSO). In literature, CSO technique is widely used to solve numerical optimization and feature selection problem. Using this optimization technique for medical image classification problem will be a pioneering idea. If this technique is directly used to classify the medical images, it provides poor results. Hence, appropriate enhancements are made on the original algorithm using a novel controlled randomness optimization algorithm and control parameter tuning. Cross‐over and Rooster selection methods are also implemented in cascaded manner for further performance improvization. All the experiments are made for two cases: with and without statistical features. The brain MRI images of 65 ND and 52 DEM subjects obtained from the Open Access Series of Imaging Studies website are used in this analysis. The ICSO without statistical features provides the highest accuracy of 86.32%, whereas the original chicken swarm optimization technique provides the accuracy of 52.13% and 52.99% with and without statistical features, respectively. [ABSTRACT FROM AUTHOR]

Summary: Computational intelligence techniques have widespread applications in the field of engineering process optimization, which typically comprises of multiple conflicting objectives. An efficient hybrid algorithm for solving multi‐objective optimization, based on particle swarm optimization (PSO) and artificial bee colony optimization (ABCO) has been proposed in this paper. The novelty of this algorithm lies in allocating random initial solutions to the scout bees in the ABCO phase which are subsequently optimized in the PSO phase with respect to the velocity vector. The last phase involves loyalty decision‐making for the uncommitted bees based on the waggle dance phase of ABCO. This procedure continues for multiple generations yielding optimum results. The algorithm is applied to a real life problem of intercity route optimization comprising of conflicting objectives like minimization of travel cost, maximization of the number of tourist spots visited and minimization of the deviation from desired tour duration. Solutions have been obtained using both pareto optimality and the classical weighted sum technique. The proposed algorithm, when compared analytically and graphically with the existing ABCO algorithm, has displayed consistently better performance for fitness values as well as for standard benchmark functions and performance metrics for convergence and coverage. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, the Orthogonal Multiple Access (OMA) principle has utilized for allocating proper radio resources in wireless networks. However, as the count of users rises, OMA‐based approaches may not satisfy the stringent emerging requirements including very low latency, very high spectral efficiency, and massive device connectivity. Moreover, there are significant challenges in cellular‐enabled Machine‐to‐Machine (M2M) communications due to the unique features of M2M‐based applications. In order to overwhelm these challenges, non‐orthogonal multiple access (NOMA) principles emerge as a solution to enhance the spectral efficiency while allowing some degree of multiple access interference at receivers. Hence, this paper intends to develop an optimal resource allocation mechanism for M2M communication. Here, the nonlinear energy harvesting performed with the aid of an accessing technology termed as NOMA. The key objective of the proposed resource allocation model is the minimization of the total energy consumption of the network. For attaining the minimized power consumption, the time allocation, and transmission power of NOMA is optimally tuned by a hybrid optimization algorithm. The proposed hybrid algorithm merges the beneficial concepts of Rider Optimization Algorithm (ROA) and FireFly (FF) algorithm and implements a new algorithm termed as FireFly Modified Bypass‐based Rider Optimization Algorithm (FMB‐ROA). Finally, the analysis of total energy concerning various constraints validates the performance of the proposed model over conventional models. [ABSTRACT FROM AUTHOR]

This paper considers the queue-aware optimal energy minimisation sparse beamforming design (QESB) in a downlink cloud radio access network (C-RAN) system where multi-RRH communicates with multi-user through a central computing cloud via digital front-haul links. The problem is formulated as the joint optimisation problem of the transmission energy consumption, system queue length and front-haul cost with sparse beamforming design. As we know, the beamforming design adaptive to both QSI and CSI is challenging because of the high complexity. Apart from previous works that take queue length as constraints, in this paper we directly minimise the queue length state involved joint optimisation problem with SINR constraints. A smooth function is proposed to approximate the l0-norm function which is discrete and non-convex. To overcome the challenge due to the non-convexity of the optimisation problem, the semidefinite relaxation (SDR) technology is utilised to convert the primitive problem into the difference of convex (DC) programming problem, and convex and concave procedure (CCP) algorithm is used to induce the sparsity of the beamforming control. The simulation results show that the scheme proposed by this paper can obtain a good tradeoff between system energy consumption, queue length and front-haul cost with SINR constraints in C-RAN system. [ABSTRACT FROM AUTHOR]

This paper presents a topology optimization method using a greedy algorithm for submodular maximization. This method is based on a shape representation using the normalized Gaussian network. The weight coefficients of Gaussians are discretized to +1/−1, and then their values are greedily inverted. Hence, the computational cost of the present method is relatively smaller than that of evolutionary algorithms. The present method is applied to a magnetic shield optimization problem. It is shown that Pareto solutions can be obtained by the present method. In addition, it can be found from the numerical results that the stochastic greedy algorithm can effectively reduce the computational time compared with the conventional greedy algorithm. As a result, it is shown that a 3‐D optimization problem with over 3000 design variables can be solved within acceptable computational time. [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]

A mobile robot is an autonomous agent, capable of planning the path from source to destination in both, a known, or an unknown environment. In this paper, we presented a novel approach to find the optimal trajectory. We have hybridized oppositional‐based learning (OBL) with the evolutionary invasive weed optimization (IWO) technique to generate the optimal path for different robot(s). The navigation algorithm considered in this work is intelligent enough to fulfill the objective of minimizing the path length and the time to reach its specified goal. Oppositional IWO (OIWO) algorithm mimics the colonizing behavior of weed and uses the concept of quasi‐opposite number, the concept of OBL is to find the shortest path between source and destination of mobile robot(s). An objective function has been formulated that takes care of the optimal target‐seeking behavior as well as the obstacle avoidance of the mobile robot. In this technique, OBL considers the current population generated by IWO algorithm and its opposite population simultaneously to get better and faster convergence. The simulation and experimental results prove and validate the effectiveness of developed OIWO path planning algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel system that enables a fully automatic modeling of both 3D geometry and functionality of a mechanism assembly from a single RGB image. The resulting 3D mechanism model highly resembles the one in the input image with the geometry, mechanical attributes, connectivity, and functionality of all the mechanical parts prescribed in a physically valid way. This challenging task is realized by combining various deep convolutional neural networks to provide high‐quality and automatic part detection, segmentation, camera pose estimation and mechanical attributes retrieval for each individual part component. On the top of this, we use a local/global optimization algorithm to establish geometric interdependencies among all the parts while retaining their desired spatial arrangement. We use an interaction graph to abstract the inter‐part connection in the resulting mechanism system. If an isolated component is identified in the graph, our system enumerates all the possible solutions to restore the graph connectivity, and outputs the one with the smallest residual error. We have extensively tested our system with a wide range of classic mechanism photos, and experimental results show that the proposed system is able to build high‐quality 3D mechanism models without user guidance. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of this paper is to introduce a novel dynamic multi‐objective optimization algorithm. First, after detecting the environmental changes, Borda count ranking method is applied to population in order to assign the Borda score to each individual, and then the lowest score individuals are removed from population and replaced with new created solutions. Furthermore, fuzzy adaptive multi‐objective cat swarm optimization algorithm is used to estimate the Pareto‐optimal front in which its parameters are tuned to new environment by Mamdani fuzzy rules when a change occurs. Performance of the proposed algorithm is tested on dynamic multi‐objective benchmarks and is compared with recent achievements. The simulations show the quite satisfactory results and higher performance of the proposed method in comparison with traditional approaches. [ABSTRACT FROM AUTHOR]

One of the main challenge in designing wireless networks is to ensure optimal coverage and connectivity, which can be achieved by optimally repositioning nodes in backbone network such that the total energy requirement is minimized. Therefore, an efficient optimization algorithm that converges at faster rate while minimizing the cost function (total power) is required. In this paper, we propose the use of Levenberg-Marquardt optimization algorithm to achieve assured coverage and connectivity control in backbone-based wireless networks. The Levenberg-Marquardt method combines the advantages of Cauchy steepest descent method and Newton-Raphson method and is expected to achieve faster convergence rate irrespective of initial conditions, which is the main motivation of this study. It relies on that when current solution is far from the optimal solution, then steepest descent method will be used, and as current solution approaches to optimal solution, then gradually switched to Newton-Raphson method to find the optimal solution. Extensive simulations using M A T L A B R2020 a has been performed to demonstrate the effectiveness of proposed method by measuring its performance in the number of iterations, elapsed time, and power requirement to maintain proper coverage and connectivity. The performance is compared with Cauchy steepest descent method as well as Newton-Raphson method. [ABSTRACT FROM AUTHOR]

This paper presents a new preconditioning technique for large-scale geometric optimization problems, inspired by applications in mesh parameterization. Our positive (semi-)definite preconditioner acts on the gradients of optimization problems whose variables are positions of the vertices of a triangle mesh in ℝ2 or of a tetrahedral mesh in ℝ3, converting localized distortion gradients into the velocity of a globally near-rigid motion via a linear solve. We pose our preconditioning tool in terms of the Killing energy of a deformation field and provide new efficient formulas for constructing Killing operators on triangle and tetrahedral meshes. We demonstrate that our method is competitive with state-of-the-art algorithms for locally injective parameterization using a variety of optimization objectives and show applications to two- and three-dimensional mesh deformation. [ABSTRACT FROM AUTHOR]

By the intelligent strategy of dynamic optimization in flatness target curve, the potential of the flatness control system can be completely fulfilled and the satisfying strip flatness can be speedily obtained. The traditional intervention pattern is static, open-loop, non-intelligent. In this paper, a new dynamic adjustment model of flatness target curve has been designed, by which the initial setpoint can be real-timely regulated. The closed-loop control system, equipped with the function of intelligent optimization and dynamic regulation, has been developed. A hybrid intelligent algorithm called ZOGE, in which both the searching definiteness and randomness are taken into account and the multipoint searching and single-point searching are simultaneously carried through, has been proposed. The equivalent factor of flatness target curve has been optimized by function module HYBALO, which is developed on the basis of the hybrid intelligent algorithm and is operated on CFC configuration language in SIMATIC TDC controller. The testing in the field has been conducted through a 1450 mm 5-stand tandem cold mill. The preliminary finding from the main results of testing in the field is reported: by means of the intelligent compensation model of flatness target curve, the content flatness quality of strip with thin thickness in slow rolling speed is effectively guaranteed. The maximum compensation efficiency of speed is 72.19%. The maximum compensation efficiency of thickness is 98.37%. The ability of eliminating strip flatness defect in dynamic regulation mode of flatness target curve is more powerful than the ability of eliminating strip flatness defect in conventional regulation mode. [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]

This paper is concerned with the tight enclosure of matrix multiplication AB for two floating-point matrices A and B. The aim of this paper is to compute component-wise upper and lower bounds of the exact result C of the matrix multiplication AB by floating-point arithmetic. Namely, an interval matrix enclosing C is obtained. In this paper, new algorithms for enclosing C are proposed. The proposed algorithms are designed to mainly exploit the level 3 operations in BLAS. Although the proposed algorithms take around twice as much costs as a standard algorithm promoted by Oishi and Rump, the accuracy of the result by the proposed algorithms is better than that of the standard algorithm. At the end of this paper, we present numerical examples showing the efficiency of the proposed algorithms. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aabstract We consider a tangent-space representation of surfaces that maps each point on a surface to the tangent plane of the surface at that point. Such representations are known to facilitate the solution of several visibility problems, in particular, those involving silhouette analysis. In this paper, we introduce a novel class of distance fields for a given surface defined by its tangent planes. At each point in space, we assign a scalar value which is a weighted sum of distances to these tangent planes. We call the resulting scalar field a ‘tangential distance field’ (TDF). When applied to triangle mesh models, the tangent planes become supporting planes of the mesh triangles. The weighting scheme used to construct a TDF for a given mesh and the way the TDF is utilized can be closely tailored to a specific application. At the same time, the TDFs are continuous, lending themselves to standard optimization techniques such as greedy local search, thus leading to efficient algorithms. In this paper, we use four applications to illustrate the benefit of using TDFs: multi-origin silhouette extraction in Hough space, silhouette-based view point selection, camera path planning and light source placement. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm for the unconstrained optimization problem, which is derived as an application of the control theory called Direct Gradient Descent Control. The static optimization problem is solved by using a dynamic controller in which the convergence speed can be greatly accelerated. The major idea is to consider the objective function F(x) and its time derivative dF(x(t))/dt as the evaluation criteria, and to apply the gradient descent method. It is verified by simulation that the proposed dynamic mathematical programming has a very great capability to converge to the optimal solution. It is also interesting to note that the method has to a certain extent the ability to find not only local optimal solutions, but also global optimal solutions. This paper improves the basic algorithm of dynamic mathematical programming so that it is suited to global optimization. The new algorithm generates movement toward the global optimal solution by utilizing two different trajectories with interaction (the chaotic trajectory and convergence trajectory). It is verified by simulation that the new algorithm functions as a global optimization procedure with a high success probability. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 88(6): 20–27, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20094 [ABSTRACT FROM AUTHOR]

This paper points out that meta-heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle swarm optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into PSO on the basis of the proximate optimality principle (POP). In this paper, we propose adaptive PSO and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 41–49, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20077 [ABSTRACT FROM AUTHOR]

The first part of the paper presents the implementation and performance of a new absorbing boundary condition (ABC) for truncating finite element meshes. This ABC can be applied conformally to the surface of the structure for scattering and antenna radiation calculations. Consequently, the computational domain is reduced dramatically, thus allowing the simulation of much larger structures, and results are presented for three-dimensional bodies. The latter part of the paper discusses optimization issues relating to the solver's CPU speed on parallel and vector processors. It is shown that a jagged diagonal storage scheme leads to a four-fold increase in the FLOP rate of the code, and a standard matrix profile reduction algorithm substantially reduces the inter-processor communication. [ABSTRACT FROM AUTHOR]

Vector quantization is known widely as a highly efficient coding for image and speech signals. The algorithm proposed by Linde, Buzo, and Gray (the LBG algorithm) is the most fundamental design procedure for this kind of quantizer. However, the LBG algorithm usually gives only the local minimum of the distortion measure based on the quantization error. With such a background, this paper introduces the simulated annealing procedure into the design of the vector quantizer based on the LBG algorithm, which is known as the effective method for the NP-complete probabilistic combinational optimization problem. The widely employed mean-square error is adopted as the distortion measure. Various approaches are considered to derive the global minimum of the measure. In other words, this paper is based on the LBG algorithm and presents a design procedure for the vector quantizer (more generally, the vector quantizer considering the channel error) by introducing simulated annealing. By this approach, a vector quantizer with a better performance than the traditional design as well as a more ideal highly efficient coding is realized. A simulated experiment is executed for the standard image data (SIDBA), and it is demonstrated that the simulated annealing is useful in the design of the vector quantizer. [ABSTRACT FROM AUTHOR]

As a method of solving the electromagnetic scattering by perfectly conducting cylinders, the discrete singularity method is discussed. For this method, a finite number of singular points is distributed within the scatterer and the wave function composed of a linear combination of solutions (each of which is derived from a helmholtz equation with each single singularity among the many) is matched with the incident wave function on the boundary. The remaining important problem in this method is how to distributed these singularities in order to obtain the accurate solution with faster convergence and with fewer number of singular points for an arbitrary configuration of the scatterer. The purpose of this paper is to present a reasonable solution to this problem. The essential of the method is that by making as unknown quantities, not only the unknown amplitude coefficients involved in the linear combination of the solutions, but also the coordinates of the singularities, the scattered wave is matched on the boundary in the sense of the least squares. This problem is linear in the amplitude coefficient but nonlinear in the singularity coordinate. Thus we solve this problem by using the algorithm by marquardt et al, of a nonlinear optimization process. This paper considers the general description and indicates its effectiveness by showing a few examples of the scattering problem of rectangular cylinders. [ABSTRACT FROM AUTHOR]

This paper presents a method for obtaining preventive control strategies based on nonlinear optimization for power systems which will incur voltage instability when load demand increases. An algorithm is proposed to determine an appropriate and effective control action taking into account the operating constraints to widen the margin between the present operating point and a voltage collapse point, thereby improving the system state. The method proposed here is based on a complex-valued load-flow technique using the Newton-Raphson method which has been developed already by the authors. Consequently, the preventive control algorithm also can evaluate the voltage instability in the event the present operating point becomes closer to the critical point and the system state becomes unfeasible at increased loading point. Further, in optimization, two-types of objective functions are introduced so that voltage stabilization and dissolution of constraint violation can be attained simultaneously. In addition, this paper discusses the extension of the proposed preventive control to the successive control method which carries out the control action to avoid voltage collapse while the load demand is increasing. Numerical examples for various model systems demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

SUMMARY Efficient interactive foreground extraction from color images is of great practical importance in computer vision. In recent years, an approach based on optimization using graph-cuts has been widely used. However, an interactive foreground extraction approach, which is intuitive and efficient for users, is required for practical applications. In this paper, we propose a novel interactive foreground extraction method based on color saliency. In our method, the user provides only some foreground pixels as the initial reference. To achieve extraction of multiple target objects, the likelihoods of the foreground and background are defined by a Gaussian mixture model and a color saliency map based on the provided reference pixels. These likelihoods are added to the cost of the proposed graph. Finally, image segmentation is performed by optimizing the proposed graph using the graph-cut algorithm. [ABSTRACT FROM AUTHOR]

Optimal design problems for semiconductor devices with relevant thermal effects can be formulated by help of the energy transport model. In this paper we perform a sensitivity analysis to derive the first-order necessary condition for the optimization. Exploiting the special structure of the KKT system we use a special variant of the classical Gummel iteration to provide a very fast optimization algorithm. Numerical results for a ballistic diode underline the feasibility of our approach. [ABSTRACT FROM AUTHOR]

Supply chain formation is the process by which a set of producers within a network determine the subset of these producers able to form a chain to supply goods to one or more consumers at the lowest cost. This problem has been tackled in a number of ways, including auctions, negotiations, and argumentation-based approaches. In this paper we show how this problem can be cast as an optimization of a pairwise cost function. Optimizing this class of energy functions is NP-hard but efficient approximations to the global minimum can be obtained using loopy belief propagation (LBP). Here we detail a max-sum LBP-based approach to the supply chain formation problem, involving decentralized message-passing between supply chain participants. Our approach is evaluated against a well-known decentralized double-auction method and an optimal centralized technique, showing several improvements on the auction method: it obtains better solutions for most network instances which allow for competitive equilibrium (Competitive equilibrium in Walsh and Wellman is a set of producer costs which permits a Pareto optimal state in which agents in the allocation receive non-negative surplus and agents not in the allocation would acquire non-positive surplus by participating in the supply chain) while also optimally solving problems where no competitive equilibrium exists, for which the double-auction method frequently produces inefficient solutions. [ABSTRACT FROM AUTHOR]

A new procedure (COVMAP) has been developed with the aim of recovering the full structure from very poor models, such as those provided by direct methods in unfavorable conditions. The procedure is based on the concept of covariance between points of an electron density map, mathematically set out by the authors in a recent paper: i.e. the density at one point depends on the density at another point of the map if their covariance is not vanishing. This concept suggested a procedure of electron density modification that uses pairs of model peaks to restrict the region where the density modification should be applied. Such modified densities lead to additional peaks, which in turn are submitted to two other important phasing tools present in EXPO2011, the resolution bias minimization and weighted least-squares procedures, which relocate, refine or reject these peaks. The procedure is cyclic and often leads to the correct structure even if the starting model is very poor. [ABSTRACT FROM AUTHOR]

In this paper, Bayesian-Networks (BN) and Ant Colony Optimization (ACO) techniques are combined to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work is that the amount of pheromones released is variable. This amount is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required. By using ACO and BN, a fitness function, responsible of automatically selecting the next course in the learning graph, is defined. This is done by generating a path that maximizes the probability of each user's success in the course. Therefore, the path can change to improve learners' average performance, taking into account the pedagogical weight of each learning unit and the social behavior of the system. Furthermore, a discrete dynamical system is obtained and its stability is studied. How to wrap an existing Learning Management System is also described in this work. Finally, an experiment compares this approach with the old on-line learning system being used previously. [ABSTRACT FROM AUTHOR]

SUMMARY The integer least squares problem is known to be NP-hard, and the algorithms such as the sphere decoding algorithm, which give the optimal solution, are usually too slow. To obtain a solution efficiently one may use one of the suboptimal algorithms such as the ordered successive interference cancellation (OSIC) algorithm or the LLL-aided OSIC algorithm that first modifies the system of equations using the LLL algorithm due to Lenstra, Lenstra, and Lovász. However, these suboptimal algorithms still may not be fast enough depending on the applications. In this paper we present two decoupling techniques to speed-up the LLL-aided OSIC algorithm. Our LLL-aided decoupled OSIC algorithm, which is applicable to clustered integer least squares problems, has the accuracy comparable to the ordinary LLL-aided OSIC algorithm (without decoupling), but is much faster than the OSIC algorithm or the LLL-aided OSIC algorithm. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a 3D-mesh segmentation algorithm based on a learning approach. A large database of manually segmented 3D-meshes is used to learn a boundary edge function. The function is learned using a classifier which automatically selects from a pool of geometric features the most relevant ones to detect candidate boundary edges. We propose a processing pipeline that produces smooth closed boundaries using this edge function. This pipeline successively selects a set of candidate boundary contours, closes them and optimizes them using a snake movement. Our algorithm was evaluated quantitatively using two different segmentation benchmarks and was shown to outperform most recent algorithms from the state-of-the-art. [ABSTRACT FROM AUTHOR]

Due to 3D occlusion, the specification of proper opacities in direct volume rendering is a time-consuming and unintuitive process. The visibility histograms introduced by Correa and Ma reflect the effect of occlusion by measuring the influence of each sample in the histogram to the rendered image. However, the visibility is defined on individual samples, while volume exploration focuses on conveying the spatial relationships between features. Moreover, the high computational cost and large memory requirement limits its application in multi-dimensional transfer function design. In this paper, we extend visibility histograms to feature visibility, which measures the contribution of each feature in the rendered image. Compared to visibility histograms, it has two distinctive advantages for opacity specification. First, the user can directly specify the visibilities for features and the opacities are automatically generated using an optimization algorithm. Second, its calculation requires only one rendering pass with no additional memory requirement. This feature visibility based opacity specification is fast and compatible with all types of transfer function design. Furthermore, we introduce a two-step volume exploration scheme, in which an automatic optimization is first performed to provide a clear illustration of the spatial relationship and then the user adjusts the visibilities directly to achieve the desired feature enhancement. The effectiveness of this scheme is demonstrated by experimental results on several volumetric datasets. [ABSTRACT FROM AUTHOR]