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In this paper, an efficient computational framework for the full-wave design by optimization of complex microwave passive devices, such as antennas, filters, and multiplexers, is described. The framework consists of a computational engine, a 3D object modeler, and a graphical user interface. The computational engine, which is based on a finite element method with curvilinear higher-order tetrahedral elements, is coupled with built-in or external gradient-based optimization procedures. For speed, a model order reduction technique is used and the gradient computation is achieved by perturbation with geometry deformation, processed on the level of the individual mesh nodes. To maximize performance, the framework is targeted to multicore CPU architectures and its extended version can also use multiple GPUs. To illustrate the accuracy and high efficiency of the framework, we provide examples of simulations of a dielectric resonator antenna and full-wave design by optimization of two diplexers involving tens of unknowns, and show that the design can be completed within the duration of a few simulations using industry-standard FEM solvers. The accuracy of the design is confirmed by measurements. [ABSTRACT FROM AUTHOR]

Abstract: As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda–Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda–Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems’ components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200tons of paper per day. The results computed by the proposed approach are compared with the Lambda–Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

Abstract: The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm. [Copyright &y& Elsevier]

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes. [ABSTRACT FROM AUTHOR]

This paper proposes a method for dynamically controlling the working slope configuration in seasonal stripping open-pit coal mines. Constructing a bench group working slope model calculates the cyclic advancement time of the stripping working slope. A mathematical optimization model is then established to minimize the transport work of the overburden, and a Sequential Quadratic Programming (SQP) algorithm is employed to solve it. The method is applied to the Shengli West No. 2 open-pit coal mine, and the results show that the number of benches in the bench group is 5. The working bench width is 89 m, and the working slope advancement meets the stripping and mining engineering continuity requirements while minimizing the transport work of the overburden. The maximum advancement speed of the working slope under this configuration is 320 m/year. The paper also analyzes the working slope's maximum advancement speed variation with changes in the bench combination form and cyclic advancement distance. [ABSTRACT FROM AUTHOR]

To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals. Therefore, the application of ISMO is validated by ten benchmark functions and feature selection. It is proved that the proposed method can resolve the problem of feature selection. [ABSTRACT FROM AUTHOR]

The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources, and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries. The existing RippleNet model doesn't consider the influence of key nodes on recommendation results, and the recommendation accuracy is not high. Therefore, based on the RippleNet model, this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model. The performance of the model is verified by experiments, which provide a theoretical basis for the promotion and recommendation of its cultural products of universarchives, solve the problem that RippleNet doesn't consider the influence of key nodes on recommendation results, and improve the recommendation accuracy. This paper also combs the development course of archival cultural products in detail. Finally, based on the Cn-RippleNet model, the cultural effect of university archives is recommended and popularized. [ABSTRACT FROM AUTHOR]

Demand side management (DSM) is one of the trending economic strategies which shifts the elastic demand to the off-peak hours from the peak hours so as to reduce the overall generation cost of the system. The work done in this paper can be categorized in three phases. In the first phase, various wind speed to power conversion mathematical models available in literature are analysed to find out the one with maximum level of wind penetration. For second phase, an economic DSM strategy is implemented to restructure the forecasted load demand model for various participation levels. In the final phase the cost-effective optimization of two microgrid distribution systems are percolated. As an optimization tool, novel hybrid CSAJAYA has been used to carry on the study. Different types of grid participating and pricing strategies along with valve point loading effect and wind energy uncertainty are considered to amplify the complexity and practicality of the study. The generation costs reduced from 3 to 5% when the forecasted demand was reformed with 20% DSM participation for both the test systems. A detailed comparison with the results from various optimization tools studied confirms the effectiveness of the proposed hybrid approach. The hybrid optimization tool presented in this paper performs better in terms of central tendencies, nonparametric statistical analysis, and algorithm execution time. • Five different wind speed-to-power mathematical modelling have been studied. • Effects of DSM in reducing the microgrid generation cost was analysed. • Grid participation strategies and their impact on microgrid generation cost was analysed. • Grid pricing strategies and their impact on microgrid generation cost was analysed. [ABSTRACT FROM AUTHOR]

This paper aims to design a Model Predictive Control (MPC) law based on the time series data gathered from the input and output of a system. An Auto-Regressive Integrated Moving Average (ARIMA) model with unknown parameters and an unknown sequence of controller signal are considered for the system. Based on a window of data, an optimization problem is formulated which can find the optimal unknown model parameters and controller sequence, simultaneously. This problem is a non-convex optimization problem with many non-convex constraints and difficult to solve. Therefore, a transformation is developed which can transfer the optimization problem to an equivalent problem with convex constraints and a non-convex objective function. This new problem is much easier to solve with the present solvers. The effectiveness of the overall approach is proved via several examples that reveal satisfaction and convincingness. • The proposed scheme designs a predictive controller using data of a nonlinear system. • Based on data, the proposed scheme determines the model and controller sequence. • The proposed optimization problem is nonconvex and difficult to solve. • The scheme is exactly transformed to a problem with convex constraints. • The new problem can be solved by convex optimization techniques. [ABSTRACT FROM AUTHOR]

Recommender Systems have been widely employed in information systems over the past few decades, making it easier for each user to choose their own products based on their past behaviour. Data mining tasks and visualization tools regularly use clustering techniques in the scientific and commercial arenas. It has been shown that clustering-based methods are effective and scalable to big data sets. The accuracy and coverage of clustering-based recommender systems are, however, somewhat low. In this paper, we suggest an improved multi-view clustering method for the recommendation of items in social networks to overcome these problems. To create better partitions, the artificial Bees colony optimization algorithm (ABC) is first used to improve the initial medoids' selection. After that, users are clustered iteratively using views of both rating patterns as well as social information using multiview clustering (MVC) (i.e. trust and friendships). Ultimately, a framework is suggested for evaluating the various options. This research study suggests a novel MVC clustering approach using the ABC optimization technique. The proposed ABC-MVC algorithm's usefulness in terms of enhancing accuracy is demonstrated by experimental findings performed on a real-world dataset and it is observed that it performs better than the pre-existing techniques and baselines. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer Programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case. [ABSTRACT FROM AUTHOR]

This research is concerned with finding the optimal solution of a job shop scheduling problem (JSSP) in a shorter time. As a result of recent trends such as Industry 4.0, machine learning has received a lot of attention, and job shop scheduling methods using machine learning techniques have been developed. We have focused on hybridization of machine learning and mathematical optimization. A JSSP can be formulated as a 0-1 mixed integer programming, and it is expected that the optimal solution can be obtained in a shorter time by providing a good initial solution for the optimization algorithm by means of a learner constructed with data of scheduling performed previously. In our method based on this idea, the processing times of all operations are used as input features to predict a good value of each binary variable which represents the precedence relationship between two operations processed on the same machine. However, the information about the operations which are not related to the intended two operations may work as noise in learning and prediction. In this paper, two limitation methods of input features are proposed. In one of the methods, the processing times of only the operations processed on the same machine that the intended operations are processed are input to the learner. In the other methods, the processing times of those operations and their pre/post operations are input. Numerical experiments showed that these methods are effective at least from the point of view of efficient construction of a learner, because the number of nodes in the input and hidden layers can be reduced. From the aspect of better learning and prediction, both of the methods worked well, that is, they could find the optimal solution in a shorter time than the previous method in the cases where the previous method could not achieve good prediction and took a relatively long solution time. However, in the cases where the previous method could solve the problem in a relatively short time, the limitation methods could not predict a good initial solution and took a relatively long solution time. Between the results of the two limitation methods, there were no difference. These results imply that the processing times of the pre/post operations do not strongly affect performance on learning and prediction and that it is important to analyze what makes a difference between the two cases and utilize the proposed methods in the former case. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a crucial task in machine learning applications, which aims to select the most appropriate feature subset while maintaining high classification accuracy with the minimum number of selected features. Despite the widespread usage of metaheuristics as wrapper-based FS techniques, they show reduced effectiveness and increased computational cost when applied to high-dimensional datasets. This paper presents a novel Binary Object-Oriented Programming Optimization Algorithm (BOOPOA) for FS of high dimensional datasets, where the Object-Oriented Programming Optimization Algorithm (OOPOA) is a novel optimization technique inspired by the inheritance concept of Object-Oriented programming (OOP) languages. The effectiveness of this method in solving high dimensional FS problems is validated by using 26 datasets, most of which are of high dimension (large number of features). Seven existing FS algorithms are compared with the proposed OOPOA using various metrics, including best fitness, average fitness (AVG), selection size, and computational time. The results prove the superiority of the proposed algorithm over the other FS algorithms, having an average performance of %92.5, 0.078, 0.084, %38.9, and 8.6 min for classification accuracy, best fitness, average fitness, size reduction ratio, and computational time. The outcomes demonstrate the proposed FS approach's superiority over currently used methods. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper presents the adaptation process of an Ant Colony Optimization system to the task of composing music. The main aims of the study include designing and implementing the above-mentioned system, conducting research, measuring the obtained results, and drawing conclusions. The application was implemented in Python and it allows you, based on the loaded MIDI file, to generate new melodies played on various instruments. The way of composing a melody is configurable by modifying the parameters of the ACO algorithm and different composition modes. To measure the quality of the obtained results, an evaluation function was created, which is used in research to measure the correlation between the parameters of the system. Based on the conducted research, it was determined how individual parameters affect the results obtained by the system. The paper covers the presentation of theoretical knowledge, system design, implementation description, research, and conclusion. [ABSTRACT FROM AUTHOR]

Business Analytics - which unites Descriptive, Predictive and Prescriptive Analytics - represents an important component in the framework of Big Data. It aims to transform data into information, enabling improvements in making decisions. Within Big Data, optimization is mostly related to the prescriptive analysis, but in this paper, we present one of its applications to a predictive analysis based on regression in fuzzy environment. The tools offered by a regression analysis can be used either to identify the correlation of a dependency between the observed inputs and outputs; or to provide a convenient approximation to the output data set, thus enabling its simplified manipulation. In this paper we introduce a new approach to predict the outputs of a fuzzy in - fuzzy out system through a fuzzy regression analysis developed in full accordance to the extension principle. Within our approach, a couple of mathematical optimization problems are solve for each desired a-level. The optimization models derive the left and right endpoints of the a-cut of the predicted fuzzy output, as minimum and maximum of all crisp values that can be obtained as predicted outputs to at least one regression problem with observed crisp data in the a-cut ranges of the corresponding fuzzy observed data. Relevant examples from the literature are recalled and used to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

An Inertial Navigation System (INS) is a standalone system that provides continuous navigation information on the position, velocity, and attitude in various conditions. Gyroscopes and accelerometers are essential components of INS. However, these sensors are prone to errors that accumulate over time and degrade the accuracy of the INS. Therefore, enhancing the performance of INS is a challenging problem. This paper aims to address this problem by proposing a method to reduce errors in real-time processing. The method uses Lifting Wavelet Transform (LWT), a popular denoising technique that belongs to the wavelet family and has an advantage over the classic wavelets in terms of time and computation complexity. Moreover, the method employs an optimization technique based on the Genetic algorithm as an intelligent averaging for combining different multi-level LWTs, which significantly improves the accuracy and efficiency of INSs. Finally, a new and implementable structure is introduced for denoising inertial sensors. The results show that the proposed method achieves an 83% and 59% improvement in gyroscope and accelerometer data for dynamic data, compared with raw data. Besides, for static data, there are 71% and 36% improvements in gyroscope and accelerometer data, respectively. [ABSTRACT FROM AUTHOR]

In the realm of aviation, jet propulsion systems serve to provide enhanced maneuverability and to make sure that the aircraft thrust is accurately and precisely regulated during take-off and landing operations. The movement of aerodynamic control surfaces (flaps, slats, elevators, ailerons, spoilers, wing attachments) determines the mobility of practically all aircraft types. Recognized as dependable components in the aviation world for take-off and landing tasks, these control surfaces are being replaced by fluidic thrust vectoring (FTV) systems, especially in small unmanned aerial vehicles (UAVs) and short or vertical take-off and landing aircraft. The FTV system is capable of directing thrust in any preferred direction without the need for any movable components. This paper numerically examines the FTV system by utilizing computational fluid dynamics (CFD) and an optimization technique based on gradients of the system components to understand the physics of the Coanda effect in FTV systems. This research employs gradient-based optimization for nozzle design in order to optimize the parameter space for different velocity ratios (VR) by calculating the moment around the upper Coanda surface, which is used to represent the jet deflection angle. In that context, four different Coanda surface-pintle pair designs for four different VRs are produced. The parameter space shows significant improvement in all four configurations, and results reveal that all output parameters successfully delay separation on the thrust vectoring system's upper Coanda surface. Finally, four optimum design suggestions are tested at various VRs, and the most efficient and proper design is recommended based on output parameters. [ABSTRACT FROM AUTHOR]

Case-based planning is an approach to planning where previous planning experience provides guidance to solving new problems. Such a guidance can be extremely useful, or even necessary, when the new problem is very hard to solve, or the stored previous experience is highly valuable, because, e.g., it was provided or validated by human experts, and the system should try to reuse it as much as possible. To do so, a case-based planning system stores in a library previous planning experience in the form of already encountered problems and their solutions. The quality of such a plan library critically inuences the performance of the planner, and therefore it needs to be carefully designed and created. For this reason, it is also important to update the library during the lifetime of the system, as the type of problems being addressed may evolve or differ from the ones the library was originally designed for. Moreover, like in general case-based reasoning, the library needs to be maintained at a manageable size, otherwise the computational cost of querying it grows excessively, making the entire approach ineffective. In this paper, we formally define the problem of maintaining a library of cases, discuss which criteria should drive the maintenance, study the computational complexity of the maintenance problem, and propose offline techniques to reduce an oversized library that optimize different criteria. Moreover, we introduce a complementary online approach that attempts to limit the growth of the library, and we consider the combination of offline and online techniques to ensure the best performance of the case-based planner. Finally, we experimentally show the practical effectiveness of the offline and online methods for reducing the library. [ABSTRACT FROM AUTHOR]

The core principle of Variational Inference (VI) is to convert the statistical inference problem of computing complex posterior probability densities into a tractable optimization problem. This property enables VI to be faster than several sampling-based techniques. However, the traditional VI algorithm is not scalable to large data sets and is unable to readily infer out-of-bounds data points without re-running the optimization process. Recent developments in the field, like stochastic-, black box-, and amortized-VI, have helped address these issues. Generative modeling tasks nowadays widely make use of amortized VI for its efficiency and scalability, as it utilizes a parameterized function to learn the approximate posterior density parameters. In this paper, we review the mathematical foundations of various VI techniques to form the basis for understanding amortized VI. Additionally, we provide an overview of the recent trends that address several issues of amortized VI, such as the amortization gap, generalization issues, inconsistent representation learning, and posterior collapse. Finally, we analyze alternate divergence measures that improve VI optimization. [ABSTRACT FROM AUTHOR]

Dominance breaking is a powerful technique in improving the solving efficiency of Constraint Optimization Problems (COPs) by removing provably suboptimal solutions with additional constraints. While dominance breaking is effective in a range of practical problems, it is usually problem specific and requires human insights into problem structures to come up with correct dominance breaking constraints. Recently, a framework is proposed to generate nogood constraints automatically for dominance breaking, which formulates nogood generation as solving auxiliary Constraint Satisfaction Problems (CSPs). However, the framework uses a pattern matching approach to synthesize the auxiliary generation CSPs from the specific forms of objectives and constraints in target COPs, and is only applicable to a limited class of COPs. This paper proposes a novel rewriting system to derive constraints for the auxiliary generation CSPs automatically from COPs with nested function calls, significantly generalizing the original framework. In particular, the rewriting system exploits functional constraints flattened from nested functions in a high-level modeling language. To generate more effective dominance breaking nogoods and derive more relaxed constraints in generation CSPs, we further characterize how to extend the system with rewriting rules exploiting function properties, such as monotonicity, commutativity, and associativity, for specific functional constraints. Experimentation shows significant runtime speedup using the dominance breaking nogoods generated by our proposed method. Studying patterns of generated nogoods also demonstrates that our proposal can reveal dominance relations in the literature and discover new dominance relations on problems with ineffective or no known dominance breaking constraints. [ABSTRACT FROM AUTHOR]

Digital integrated electronics benefits from its higher abstraction level, allowing optimisation methods and automated workflows. However, analogue integrated circuit design is still predominantly done manually, leading to lengthy design cycles. This paper proposes a new systematic design approach for the sizing of analogue integrated circuits to address this issue. The method utilises a surrogate optimisation technique that approximates a simple monomial function based on few simulation results. These monomials are convex and can be optimised using a simple linear optimisation routine, resulting in a single global optimal solution. We show that monomial functions, in many cases, have an analytic relation to integrated circuits, making them well suited for the application. The method is demonstrated by designing a 14 MHz pseudodifferential voltage-controlled oscillator (VCO) with minimised current consumption and is manufactured in a 180 nm process. The measured total current matches the predicted and is lower than that for other similar state-of-theart VCOs. [ABSTRACT FROM AUTHOR]

High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models. [ABSTRACT FROM AUTHOR]

Buildings where part or all of the components are prefabricated in a factory, then transported to the construction site and assembled through reliable connections, are called prefabricated buildings (PB). Considering the regional differentiation and asynchronization scheduling characteristics of PB, it is difficult for project managers to schedule and optimize construction resources - including manpower/equipment for parallel assembly projects. The purpose of this paper is to solve the multi-projects resource optimization problem under the condition of prefabricated buildings. Therefore, this paper establishes a bilevel multi-objectives model composed of project layer and work layer, which aims to shorten project duration, reduce cost, and maximize robustness. Based on this, a hybrid niche genetic-raccoon family optimization (NG-RFO) algorithm is proposed, equipped with better population diversification effect of niche genetic (NG) and strong local search ability of raccoon family optimization algorithm (RFOA). Moreover, the performance of the improved hybrid algorithm has been compared with genetic algorithm (GA) and RFOA. The actual data of a prefabricated building project group in Hebei Province has been used. The results show that the algorithm is a feasible and efficient method for obtaining a satisfactory resource scheduling scheme in limited time, which is obviously superior to GA and RFOA. This paper provides a reference for project managers to make reasonable and efficient progress plans in an uncertain environment. [ABSTRACT FROM AUTHOR]

Agriculture and its allied sector provide the livelihoods of vast rural areas in India. It also plays a vital role in the growth of developing countries like India. Rice, one of the primary components of Indian agriculture plays a significant role in the food security of the country and the world. However, it is stricken by many diseases that affect the quality and quantity of rice yield. This paper proposes a custom CNN architecture for detecting and classifying common diseases found in rice plants by reducing the number of parameters associated with the network. The proposed CNN architecture has been trained using a dataset of four types of common rice plant diseases. In addition, 1400 on-field images of a healthy rice leaf image dataset have been introduced in the paper for the detection of disease-free plants as well. Independent experiments were carried out with and without the inclusion of the healthy leaf image dataset. The performance of the proposed model is evaluated with the use of Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam) optimization techniques using several performance matrices. Experimental results from the dataset for the classification of four rice plant diseases show that the model using SGDM optimization gives maximum accuracy of 99.66% and the model using the Adam optimization gives maximum accuracy of 99.83% on the test set in the 7th epoch respectively. However, the model with Adam optimizer performed better than the model with SGDM optimizer when the healthy leaf image dataset is included giving a maximum accuracy of 99.66% and 97.61% in the 7th epoch respectively. [ABSTRACT FROM AUTHOR]

This paper deals with the multiobjective optimization problem of an agro- alimentary production workshop. Three criteria are considered in addition to this initial cost of production: the cost of the out-of-date products, the cost of the distribution discount and the makespan, and a new coding is proposed for this type of workshop. The adopted approach consists in generating optimal solutions diversified in the search space of solutions, and to help the decision maker when it cannot give a particular preference to one of the objective functions to make the good decision with respect to the quoted criteria. [ABSTRACT FROM AUTHOR]

The first stage in designing an efficient solid oxide fuel cell (SOFC) model is to accurately determine its unknown parameters. The modeling and analysis of the steady-state and dynamic responses of SOFC models require precise and reliable parameter identification. As a result, this paper proposes four novel metaheuristic optimization algorithms to extract the parameters of a reliable SOFC and build an accurate model whose response matches the actual measured response. The paper considers the following optimization algorithms: Gorilla troops optimizer (GTO), Honey badger algorithm (HBA), African vultures optimization algorithm (AVOA), and Chimp optimization algorithm (ChOA). These recent algorithms have the benefits of providing a suitable balance between the global exploration and the local phases of exploitation that improve the population variety and efficiently reach a global optimal solution. These algorithms are used to determine the optimal parameters of the SOFC steady-state model using a set of a Siemens cell experimental dataset at various operating temperatures. Moreover, a Proportional-Integral-Derivative (PID) controller is proposed in the dynamic model to improve the transient response of a Siemens 100 kW stack voltage and current at different power levels. Furthermore, extensive comparisons based on statistical analysis are used to validate the results of the proposed optimizers. The proposed optimizers are then compared with other optimization techniques presented in previous literature. The results show that the GTO and HBA optimizers outperform other algorithms in determining the best SOFC parameters that produce accurate voltage-current and power-current characteristic curves. [ABSTRACT FROM AUTHOR]

This manuscript discusses the positional systems used in intelligent robots. One of the characteristic problems that arise in such systems is associated with friction, including sticking during the process. Friction greatly affects the positioning accuracy of the actuators of controlled robots, especially if a pneumatic actuator is used as a motor. The work is devoted to the study and solution of this problem. Since it is impossible to completely eliminate the effect of friction, ways to minimize this effect are being investigated using a targeted choice of system parameters. For this purpose, the method of multicriteria optimization of a detailed mathematical model is applied on the example of a pneumatic positional system, as the most sensitive to friction. The paper considers the first stage of optimization associated with visualization and preliminary analysis of the process in order to prepare it for performing a system procedure. The dynamics equations of an intelligent positional system are written out, both in full-size and dimensionless one. The most convenient mathematical model of friction is presented terms of its use in the process of optimizing the entire control system. A procedure for visualizing the optimization process in the form of a graphical interface has been developed. The first stage of the intelligent drive system optimization procedure has been carried out. The paper presents at which parameters of the control system the "stick-slip" effect has less influence on the positioning process. [ABSTRACT FROM AUTHOR]

This paper presents a novel Adaptive Mutation Quantum-inspired Squirrel Search Algorithm (AM-QSSA). Firstly, based on the population mutation, a location-update of quantum state correlative and attractor method is proposed. By introducing a random process to modify the sliding factor of the local attractor, the inevitable lack of diversity in the population renewal method is solved. The premature convergence problem of adaptive mutation rate improvement algorithm based on squirrel position update mode is introduced. Meanwhile, the paper decomposes the location update process of the SSA, and improves it with quantum-behavior. Furthermore, it proposes a novel quantum-inspired squirrels search algorithm. This method finds the complementary effect between quantum behavior and squirrel search algorithm, and solves the problem of premature convergence probability of SSA. In addition, it improves population diversity, and achieves the balance between global and local search. The efficiency of the proposed AM-QSSA is evaluated using exploitation analysis, exploration analysis, success rate analysis, convergence rate analysis on classical benchmark functions as well as Congress on Evolutionary Computation (CEC) 2017 test functions. For further study, AM-QSSA optimizes an image registration problem for an extensive study to check its applicability. The results reveal that AM-QSSA is more efficient and stable than SSA. And it is comparable to the most advanced optimization algorithms. [ABSTRACT FROM AUTHOR]

Proposing a reliable feature selection approach is the primary stone for endorsing the prediction performance; therefore, this paper proposes an enhanced optimization technique to identify the features of diverse datasets. The proposed approach employs the memory dependency of the fractional calculus operators to boost the agents' motion during the diversification and the intensification of the hunter-prey optimization algorithm (HPO). Moreover, a chaotic sine map is applied to balance the transition between the exploitation and exploration cores, aiming to improve further convergence. As a result, a novel variant named fractional adaptive HPO (F-AHPO) is proposed as a modified feature selection (FS) model. The proposed algorithm's applicability and performance are validated by using various UCI datasets. For the purpose of evaluating the proposed approach performance, its results are assessed and demonstrated versus a set of state-of-art techniques representing the literature. The results show the high ability of F-AHPO to enhance classification accuracy by removing irrelevant features. F-AHPO provides better results than other methods based on several performance measures. [ABSTRACT FROM AUTHOR]

This paper presents an analytical study of radial-flux slotless limited-angle torque motors. The modeling is done based on the magnetic equivalent circuit of actuators and electromagnetic equations are employed to calculate the air-gap ux as well as the produced torque. This model is then used for designing actuators both in outer-rotor and inner-rotor structures, considering the design constraints and desired characteristics. Given that the objectives in the design stage are usually in conict with each other, an intelligent multi-objective optimization algorithm is required to design the best-fit actuators. Analytical and simulated results are presented and compared to determine the accuracy of the model and verify the design equations as well as the design approach. Given that this type of actuators is the key element in industrial control, the contributions of this paper are focused on the new analytical field solution based on magnetic equivalent circuit, design, and optimization approach to radial-ux structure and introducing a general procedure that can be extended to similar structures and actuators. [ABSTRACT FROM AUTHOR]

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application. [ABSTRACT FROM AUTHOR]

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) includes more low-cost and less-power sensor nodes. All the sensor nodes are positioned in a particular area and form a wireless network by way of self-organizing. They has the ability to work normally at any of the special or wicked environ that people cannot close. However, the data transmission among nodes in an effective way is almost not possible due to various complex factors. Clustering is a renowned technique to make the transmission of data more effective. The clustering model divides the sensor nodes into various clusters. Every cluster in network has unique cluster head node, which send the information to other sensor nodes in cluster. In such circumstances, it is the key role of any clustering algorithm to choose the optimal cluster head under various constraints like less energy consumption, delay and so on. This paper develops a new cluster head selection model to maximize the lifetime of network as well as energy efficiency. Further, this paper proposes a new Fitness based Glowworm swarm with Fruitfly Algorithm (FGF), which is the hybridization of Glowworm Swarm Optimization (GSO) and Fruitfly Optimization algorithm (FFOA) to choose the best CH in WSN. The performance of developed FGF is compared to other existing methods like Particle swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), GSO, Ant Lion Optimization (ALO) and Cuckoo Search (CS), Group Search Ant Lion with Levy Flight (GAL-LF), Fruitfly Optimization algorithm (FFOA) and grasshopper Optimization algorithm (GOA) in terms of alive node analysis, energy analysis and cost function and the betterments of proposed work is also proven. [ABSTRACT FROM AUTHOR]

Collaborative optimisation of system reserves and utilisation of renewable energy is an efficient approach to achieving robust optimal dispatch of integrated energy systems (IES). However, conventional robust dispatch methods are often too conservative and lack the ability to consider uncertainties such as renewable energy and contingency probabilities. To address these limitations, this paper proposes a distributionally robust dispatch model that co-optimises reserves and do-not-exceed (DNE) limits while considering these uncertainties. First, a deterministic optimisation model of IES is established with a minimum operational cost objective and security constraints. Next, a two-stage robust collaborative optimisation framework of IES is built, based on the Wasserstein measure, with random equipment faults represented by an adjustable ambiguity set. Finally, to overcome the computational challenges associated with robust approaches, duality theory and Karush-Kuhn-Tucker (KKT) conditions are used to convert the formulation into a mixed integer linear programming (MILP) model. The Simulation results on the modified IEEE 33-bus system demonstrate the effectiveness of the proposed model and solution methodology. [ABSTRACT FROM AUTHOR]

Economic Load Dispatch (ELD) is an effective dispatch strategy to improve economic efficiency while ensuring the safety and stability of the power system. It minimizes production costs by rationally allocating the power generated by each power system unit. In this paper, an Manta Ray Foraging Optimization (MRFO) algorithm based on the mathematical spiral foraging strategy is proposed to solve the ELD problem in power systems considering transmission losses. Eight different mathematical spirals were introduced into the MRFO algorithm's foraging strategy, including the Rose spiral, Archimedes spiral, Fermat spiral, Cycloid spiral, Hypotrochoid spiral, Epitrochoid spiral, Inverse spiral and Lituus spiral. The mathematical spiral foraging strategy can enhance the global search ability of the MRFO algorithm and improves its convergence velocity. To verify the performance of the proposed improved MRFO algorithm, 30 benchmark functions are tested and the optimization performances are compared with BOA, AOA, SCA, HHO, WOA, RSA, and GWO. The simulation experimental results show that the performance and application of the manta ray foraging optimization algorithm based on the mathematical spiral foraging strategy outperform other intelligent optimization algorithms tested on 30 benchmark functions. Finally, two ELD cases with total demands of 2500 MW and 10500 MW are selected and solved using the improved manta ray foraging optimization algorithm. Comparing the simulation results with other optimization algorithms, it is shown that the proposed improved algorithm obtains the best fuel cost and smaller transmission loss in almost every test case, which canl help to improve the economic efficiency of the power system and achieve the goal of economic load dispatch. [ABSTRACT FROM AUTHOR]

Pressure and water level control are quite challenging in grate-fired boilers due to higher combustion lag. Fluctuation in the pressure and the water level is more evident in the coal-fired grate boilers due to the presence of lower volatile and higher char. This results in suboptimal operation and poor performance of the boiler. This paper presents a novel predictive and dynamic simulation model for the drum dynamics analysis of a grate-fired boiler by combining a data-driven model and a thermodynamic model. A data-driven methodology is employed for the estimation of combustion, heat transfers, and circulation performance of the boiler. A novel thermodynamic model is proposed for the boiler dynamics of a hybrid boiler. The proposed data-driven model has been integrated with the thermodynamic model to reduce the randomness and improve consistency. Pressure and water level errors are estimated by comparing the predicted value and experimental result and the multi-objective optimisation technique is employed for the minimisation of errors. The Stochastic Gradient Descent algorithm is proposed for its ability to quick learning and adaptation to variations in fuel and combustion characteristics. The model demonstrates good accuracy in predicting the combustion and boiler dynamics of a grate-fired boiler. The proposed model has good potential to be used for the reciprocating grate solid-fuel boiler control in fluctuating load conditions. • A model for the prediction of boiler pressure and water level for grate combustion. • Integration of a data driven combustion model and the boiler dynamics model. • Multi-objective optimisation to minimise the errors in pressure and water level. • Stochastic gradient descent method to adapt with the change in fuel and combustion. • Model can be used for the control of a grate boiler in fluctuating load condition. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals. The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method. A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER's validation and testing against seven mathematical optimization problems. The results show that BER can both explore and avoid local optima. BER has also been tested on an engineering design optimization problem. The results reveal that, in terms of performance and capability, BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

3D reconstruction of objects is with interest nowadays, mainly in production industry. The main challenge of them is accuracy and processing time, especially for small and detailed objects. The field of photogrammetry realizes the 3D reconstruction of objects through 2D photos. Different software, free or non-free exists, providing different quality and performance. Accurate 3D reconstruction is important in cloning objects, especially in the industry of spare parts or in the production of prostheses in medicine, etc. Determining accurately the sizes of the object, especially those with complex geometric shapes is very important in the 3D printing process. The purpose of this paper is the analysis of the performance of 3D reconstruction in terms of accuracy for objects of different sizes regarding the number of its photos and the time evaluation of this process. The 3D reconstruction will be performed by free software Meshroom, measurement will be done in MeshLab and non-free software Agisoft. Experimental results show that quality and performance of 3D reconstruction depends on the number of photos of the object, concluding in finding the optimal balance between these parameters. By comparing obtained results from MeshRoom and Meshlag versus Agisoft, it is claimed that Agisoft performs better than MeshRoom. It offers more optimization techniques, reduces processing time, more visual quality in the reconstructed 3D object as well as more accuracy in measurement. [ABSTRACT FROM AUTHOR]

A backstepping control strategy for an underactuated quadrotor slung load system is presented in this paper. The issue of trajectory tracking for a cable-suspended load is addressed. A rigid body and a point mass are used to model the Quadrotor and slung-load, respectively. Lyapunov theory and backstepping technique are used to design the controller. Thrust and angular velocity control laws are carefully designed to ensure that the closed-loop system is asymptotically stable. The problem of finding the optimal set of parameters for the backstepping controller gains is formulated as an optimization problem and solved with the Nonlinear Backstepping Controller using Particle Swarm Optimization algorithm (NBC-PSO). The effectiveness of the optimized controller is established by simulation results done utilizing MATLAB/Simulink and compared with a conventional Nonlinear Backstepping Controller (NBC). It was observed that, the proposed NBC-PSO achieves approximately 14% improvement in load position trajectory convergence to the desired trajectory. The results confirm that the NBC-PSO controller is effective, and the controller converges faster than other controllers. [ABSTRACT FROM AUTHOR]

The use of metaheuristics is one of the most encouraging methodologies for taking care of real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence metaheuristic algorithm inspired by the intelligent hunting behavior of bald eagles. In recent research works, BES algorithm has performed reasonably well over a wide range of application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency and has a tendency to stuck in local optima which affects the final outcome. This paper introduces a modified BES (mBES) algorithm that removes the shortcomings of the original BES algorithm by incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), and Transition & Pharsor operators. OBL is embedded in different phases of the standard BES viz. initial population, selecting, searching in space, and swooping phases to update the positions of individual solutions to strengthen exploration, CLS is used to enhance the position of the best agent which will lead to enhancing the positions of all individuals, and Transition & Pharsor operators help to provide sufficient exploration–exploitation trade-off. The efficiency of the mBES algorithm is initially evaluated with 29 CEC2017 and 10 CEC2020 global optimization benchmark functions. In addition, the practicality of the mBES is tested with a real-world feature selection problem and five engineering design problems. Results of the mBES algorithm are compared against a number of classical metaheuristic algorithms using statistical metrics, convergence analysis, box plots, and the Wilcoxon rank sum test. In the case of composite CEC2017 test functions F21-F30, mBES wins against compared algorithms in 70% test cases, whereas for the rest of the test functions, it generates good results in 65% cases. The proposed mBES produces best performance in 55% of the CEC2020 test functions, whereas for the rest of the 45% test cases, it generated competitive results. On the other hand, for five engineering design problems, the mBES is the best among all compared algorithms. In the case of the feature selection problem, the mBES also showed competitiveness with the compared algorithms. Results and observations for all tested optimization problems show the superiority and robustness of the proposed mBES over the baseline metaheuristics. It can be safely concluded that the improvements suggested in the mBES are proved to be effective making it competitive enough to solve a variety of optimization problems. [ABSTRACT FROM AUTHOR]

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering. [ABSTRACT FROM AUTHOR]

"Extremely High Frequency (EHF)" and "Very high frequency (VHF)" bands are mainly utilized with "Underwater Wireless Sensor Networks (UWSNs)" for communication purposes. However, due to the mobility of underwater sensors in water because of the water tide, the EHF/VHF signals may attenuate, lose or fade depending on the condition of the water. Therefore, it is a challenging stint of finding the optimal parameters of UWSN topology planning. In this paper, three "Multi-Objective Optimization Algorithms (MOOAs)" have been utilized to mitigate this problem, namely MOSFP, SPEA2 and NSGA-II. This work also intends to minimize path loss. On the other hand, it intends to maximize the power density of the network. Various network configurations, such as distance between sender and receiver, water conductivity and water permeability, are considered to evaluate the proposed objective models. Qualitative and quantitative tests have been conducted to analyze the results. From the analysis of the intersection point of Pareto-front of the objective functions, it is shown that all the algorithms find the optimal distance between transmitter and receiver, which balances the aforementioned maximization and minimization objective functions. This value is 36 m. [ABSTRACT FROM AUTHOR]

This paper presents a binary variant of a novel physics-based meta -heuristic optimization algorithm, namely Light spectrum optimizer (LSO), for tackling both the 0–1 knapsack (KP01) and multidimensional knapsack problems (MKP). Because of the continuous nature of the standard LSO that contradicts the knapsack problem's discrete nature, two various transfer functions: S-shaped and X-shaped, are used to convert the continuous values produced by LSO into discrete ones. Some binary solutions produced by the binary LSO (BLSO) may be infeasible, so an improvement-repair strategy is used to convert those solutions into feasible ones by making some improvements on them. Moreover, the classical LSO was modified in this study to propose a new binary variant, namely BMLSO, with better exploration and exploitation operators for overcoming the knapsack problems. Additionally, a novel method, which simulates the swarm intelligence behaviors and the simulated binary crossover (SBX) to accelerate the convergence speed with avoiding stuck into local minima, has been proposed for producing a new binary variant of MLSO known as BHLSO. To verify the performance of the proposed binary variants of LSO, 45 benchmark instances of KP01 and 30 benchmark instances of MKP used commonly in the literature have been used in our experiments. The experimental findings show the superiority of BHLSO for both KP01 and MKP compared with several well-known algorithms in terms of CPU time, convergence speed, and accuracy. [ABSTRACT FROM AUTHOR]

This paper investigates the covert communication of cooperative non-orthogonal multiple access (NOMA) systems, where the near user serves as a decode-and-forward (DF) relay and the far user receives the covert information from both the source and the near user. To improve the covertness performance, cooperative jamming and power randomness are adopted. Specifically, we derive detection error probability (DEP) of Willie and the minimum detection error probability (MDEP) at the optimal decision threshold for each phase. In addition, the reliability of the proposed system is investigated by deriving closed-form expressions for the outage probability (OP) of the two users. Under the covertness and reliability constraints, an optimization algorithm to maximize the effective covert rate is designed. Simulation results have confirmed the correctness of the theoretical analysis, and the proposed scheme can achieve a better covert communication. [ABSTRACT FROM AUTHOR]

Deep learning is a powerful tool that uses simple representations to express complex ideas and allows computers to mine hidden information and value from experience. It has achieved great success in a variety of fields. However, with the development of deep learning models, the computing resources consumed by training the models increase rapidly, making it difficult to directly deploy the models to portable devices or embedded systems. Therefore, compressing and accelerating DNN models without sacrificing performance has become a crucial area of research in the field of deep learning. In many existing compression techniques, optimization theory and approaches play an important role in their research and implementation. In this paper, we focus on neural network compression from an optimization perspective and review related optimization strategies. Specifically, we summarize optimization techniques emerging from four general categories of commonly used network compression approaches, including network pruning, low-bit quantization, low-rank factorization, and knowledge distillation. Finally, we provide a summary and discuss some possible research directions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new classes of nonsmooth second-order cone-convex functions and respective generalizations in terms of first and second-order directional derivative. These classes encapsulate several already existing classes of cone-convex functions and their weaker variants. Second-order KKT type sufficient optimality conditions and duality results for a nonsmooth vector optimization problem are proved using these functions. The results have been supported by examples. [ABSTRACT FROM AUTHOR]

The production of remote sensing products is very time-consuming, because it contains a large amount of remote sensing data to be processed. To increase the production efficiency of remote sensing products, a remote sensing processing job is usually divided into multiple tasks that are executed in parallel in the cluster system. So efficient task scheduling is the core problem of remote sensing products production system. This paper issued this problem and proposed a hybrid particle swarm optimization algorithm to improve the production efficiency of the remote sensing products. First, the task scheduling model of remote sensing products was established to minimize the maximum task execution time. Then, in the framework of particle swarm optimization, an inertia weight parameters adjustment strategy based on linear decreasing was adapted. For the solution obtained in each iteration, a local search procedure of task movement between the production nodes was employed to improve the quality of the solution. In addition, a probability acceptance rule was also introduced to allow the acceptance of poor solutions avoid trapping into local optimum prematurely. Finally, we carried out several experiments in the CloudSim simulation platform. The experimental results demonstrate that the proposed algorithm is effective and more competition when compared with the existing scheduling algorithms, such as ant colony optimization, tabu search and other classical scheduling algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes the application of control by state feedback using the linear quadratic regulator (LQR) optimized by metaheuristics to damp low-frequency electromechanical oscillations in electrical power systems. The current sensitivity model was used to represent the single machine infinite bus (SMIB) system in the time domain. The weighting matrices of the LQR were adjusted using four different algorithms: (i) the genetic algorithm, (ii) the differential evolution algorithm, (iii) the particle swarm optimization algorithm, and (iv) the gray wolf optimization (GWO) algorithm. In the cases considered, disturbances were applied to the electrical power system and, then, performances comparisons associated with each metaheuristic were statistically analyzed, in which the number of iterations, error, and time to achieve convergence of each algorithm were compared. From the results, it was possible to conclude that the algorithms were efficient in adjusting the weighting matrices of the LQR, providing additional damping to the poles of interest of the system. It was also possible to conclude that the GWO algorithm presented the best performance, accrediting it as a powerful tool in the study of small-signal stability for the analyzed case. • Design of an LQR for small-signal stability analysis of power systems. • Implementation metaheuristics for adjusting the parameters of the LQR. • Comparison of the performance of evolutionary and swarm-based algorithms. [ABSTRACT FROM AUTHOR]

Improved Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been proposed to identify the parameters of solar PV cells and modules. For this purpose, the traditional PSO algorithm has been adapted and an improved one using the fractional order calculus has been especially designed as well. The proposed FODPSO together with the PSO has been applied in both the single and double diode models to describe the specified PV units for purpose of comparison. To examine the advantages of the proposed FODPSO over conventional PSO, two approaches have been performed; the first approach is implementing the FODPSO and PSO algorithms using input data obtained from previous international publications for different types of PV cells/modules, then comparing the FODPSO algorithm's results to the results of PSO and the other previous optimization techniques that were using the same input data and identical PV cells/modules. The second validation approach is achieved by performing real measurements in outdoor conditions for another two types of PV modules (mono and poly crystalline Silicon) and inserting these measurements' readings as input data to the PSO and FODPSO algorithms. A comparative study has also been done for both algorithms' results. Enhanced results have been achieved using the FODPSO technique. It is worth mentioning that during the development of this work, other efforts have been made with different techniques that may result in similar or even better results, yet this paper is still introducing for the first time the FODPSO technique (as a modified technique of the PSO) as applied on the PV modeling problem, in addition the paper presents the benefits of the proposed FODPSO over the conventional PSO and presents a comparison that shows the superiority of the FODPSO over the conventional PSO. [ABSTRACT FROM AUTHOR]

This paper deals with the physical layer security for multi-unmanned aerial vehicle (UAV) assisted transmission of integrated sensing and communication (ISAC) signals from a dual-function base station (BS) to the ground user. Here, an active eavesdropper (Eve) on the ground eavesdrops on the information transmitted by the UAV network to the user. We propose a Stackelberg game model for the game problem of information security transmission between UAV and Eve, with perfect or imperfect channel state information (CSI) and cost information. To deal with the Stackelberg game problem, the dual optimization theory and KKT conditions are used to solve it, then we prove the existence and uniqueness of Stackelberg equilibrium (SE). At the same time, considering that the influence of ISAC signal's sensing of Eve's location on the distance between Eve and the user, and thus on the utility function of both sides of the game, this paper uses the Cramer–Rao bound (CRB) to represent the accurate performance of location sensing. Then the relaxation method is used for the non-convex optimization problem, and convex optimization tools can be used subsequently. Finally we notice that both operations have their own optimal resolution strategy, it is difficult to reach agreement, therefore an overall algorithm is proposed to solve the whole problem iteratively. In the simulation results, the effect of power on game and location sensing, and the effect of the distance between Eve and the user on the utility function of both sides of the game are given. In addition, we give the relationship between the CRB threshold and the transmit power, and verify the convergence of the game and overall algorithm. [ABSTRACT FROM AUTHOR]