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In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

With the field of traffic signal control progressing towards more adaptive, and computationally efficient models, the integration of sparse travel time data and advanced optimization techniques enhances the intersection performance. This paper presents a novel optimization framework for traffic control using sparse travel time data, with a field penetration rate as low as 10%. The paper focuses on developing mathematical models that can handle the variability in arrival rates both within and across signal cycles. By utilizing delay polygons, we accurately model and minimize the total intersection delay caused by non-uniform vehicle arrivals and signals, leading to optimal signal timing solutions. The model effectively prioritizes queue dissipation for highly saturated phases while minimizing overall intersection delay. The algorithm accommodates variations in phase-wise delays across cycles, indirectly reflecting changes in traffic demand. Additionally, the sample-based design demonstrates performance comparable to volume-based dynamic design in terms of average delay, average speed, and total travel time across cycles. With the innovative use of sample re-identification data obtained through various sensor technologies, the proposed algorithm is capable of delivering optimal control of time varying traffic demand with minimal data input.

Compiler is a tool that converts the high-level language into assembly code after enabling relevant optimizations. The automatic selection of suitable optimizations from an ample optimization space is a non-trivial task mainly accomplished through hardware profiling and application-level features. These features are then passed through an intelligent algorithm to predict the desired optimizations. However, collecting these features requires executing the application beforehand, which involves high overheads. With the evolution of Natural Language Processing (NLP), the performance of an application can be solely predicted at compile time via source code analysis. There has been substantial work in source code analysis using NLP, but most of it is focused on offloading the computation to suitable devices or detecting code vulnerabilities. Therefore, it has yet to be used to identify the best optimization sequence for an application. Similarly, most works have focused on finding the best machine learning or deep learning algorithms, hence ignoring the other important phases of the NLP pipeline. This paper pioneers the use of NLP to predict the best set of optimizations for a given application at compile time. Furthermore, this paper uniquely studies the impact of four vectorization and seven regression techniques in predicting the application performance. For most applications, we show that tfidf vectorization and huber regression result in the best outcomes. On average, the proposed technique predicts the optimal optimization sequence with a performance drop of 18%, achieving a minimum drop of merely 0.5% compared to the actual best combination.

The transportation sector is one among the key sources of greenhouse gas emissions (GHGs) leading to climate change and global warming. Energy transition through electrified transportation is one of the solutions to tackle the issues. Electric vehicles (EVs) offer significant environmental and economic advantages against the conventional Internal Combustion Engine (ICE) vehicles. EVs are called mobility loads and their connectivity to the utility grid for charging is unpredictable. The large penetration of such unpredictable loads into the utility grid will lead to undesirable impacts on the utility service. This paper highlights the importance of managing and optimizing the charging schedules. The optimization of EV charging has diverse aspects, and the perspectives of EV charging differ among consumers, aggregators, and utility services. Proper planning and management of EV charging is essential to achieve harmony amongst these stakeholders. A comprehensive review on the objectives of electric vehicle charging optimization from various perspectives is presented and discussed in this paper. EV charging optimization techniques including mathematical programming, meta heuristics algorithms and machine learning techniques are explored. The main objectives, constraints, strength, and limitations of different charging optimization techniques are analyzed in detail. A brief discussion on the communication strategies for data exchange in EV charging framework is presented and the need for a communication security constrained EV charging scheduling is also emphasized.

A method for optimizing multiple responses in solenoids utilized in a magnetic nozzle assembly for an electric plasma thruster is presented. The primary goal is to minimize power usage while maintaining a sufficiently high magnetic flux density at the coil axis. This is especially critical during testing in a vacuum chamber, where heat dissipation is a challenge, especially for small components and intricate assemblies. This paper presents a method for quickly calculating the magnetic field along the solenoid axis that provides accurate estimates, independent of coil size. Additionally, we present general and concrete results for the employed magnetic nozzle design to illustrate the applied optimization method. The paper details a methodology for optimizing multiple parameters of a solenoid, including its radius, length, current, number of turns, and wire diameter. The aim is to minimize, maximize, or meet specific targets for magnetic flux density, power, or mass. Additional studies could utilize a suitable metric to optimize the homogeneity of the magnetic field of multiple coils. The coils designed using the algorithm have a radius of 17.5 mm, a length of 21 mm, consist of 300 turns with a wire diameter of 1 mm, and operate at a direct current of about 5 A. The potential maximum magnetic field is 40 mT with a power consumption of around 30 W.

Users have different sensitivities to different attributes for the same data set. Disregarding this can result in inadequate data confidentiality or reduced data availability. To address this, this paper proposes a multi-level personalized local differential privacy mechanism optimization method. In high-dimensional heterogeneous data scenario, this paper first adopts the optimal privacy budget allocation scheme to allocate the privacy budget of different attributes, and then categorizes the privacy levels into high, medium, and low. Users can freely select the privacy level for each attribute or choose the same level for all attributes. For data analysts, reorganizing data with different privacy levels to achieve histogram estimation is a challenging task. The paper introduces a histogram optimization estimation method based on two evaluation criteria. It proposes a combinatorial optimization method, OC, which minimizes mean square error, and a combinatorial optimization method, OP, based on perturbation theory, which minimizes maximum error. The paper comprehensively studies the balance between data availability and privacy protection based on these two rules.

This paper proposes the Shrike Optimization Algorithm (SHOA) as a swarm intelligence optimization algorithm. Many creatures, who live in groups and survive for the next generation, randomly search for food; they follow the best one in the swarm, a phenomenon known as swarm intelligence. While swarm-based algorithms mimic the behaviors of creatures, they struggle to find optimal solutions in multi-modal problem competitions. The swarming behaviors of shrike birds in nature serve as the main inspiration for the proposed algorithm. The shrike birds migrate from their territory in order to survive. However, the SHOA replicates the survival strategies of shrike birds to facilitate their living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modeling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019 and 12 from CEC-2022’s most recent test functions, for a total of 41 competitive mathematical test functions and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon ranking sum and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.

This study presents a comprehensive bibliometric analysis of grid-connected microgrid (MG) scheduling controller techniques. An extensive search was done in the Scopus database using preset parameters to extract articles relating to the MG scheduling controller. The selection of the most cited paper involved careful keyword filtering on grid-connected MG scheduling controllers over the period from 2013 to 2024. Within the timeframe, a total of 115 top-cited articles were extracted, focusing on the scheduling controller algorithms applied to the grid-connected MG system. These highly cited articles originated from a diverse source, encompassing 49 distinct journals, spanning 28 different regions, and representing the publications of 7 distinct publishers. This paper seeks to identify and analyze the highly referenced published articles in the relevant area to yield an in-depth analysis of advanced controllers and optimization strategies in MG energy management systems. The key challenges such as power electronic interface, quality, controller, safety and optimization were also highlighted to provide the clearest insight on the recent MG development. Valuable recommendations for future research directions are also provided, aimed at promoting the sustainable growth of MGs. A substantial total of 63.56% articles were published based on simulation while 18.6%, 13.95% and 3.87% of total articles were published on the experimental setup, critical analysis and review-based study. Therefore, it can be inferred that ongoing research and development efforts continually seek to improve the sustainability of MG systems within the electric power sector. The bibliometric analysis was employed to identify pivotal research publications concerning grid-connected MG scheduling controller technique. This analysis aimed to delineate the multidisciplinary nature, illustrate trends, and outline areas warranting further research in the field. Thus, to ensure an effective, economical, reliable, and sustainable power supply, this analysis will broaden the scope and offer context for the development of MG scheduling controller integrated grid systems.

The global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their own energy, aiming to decrease both electricity costs and carbon footprints. To support these users, many papers have been devoted to developing optimal investment strategies for residential energy systems. However, there is still a significant gap as these studies often neglect important aspects like carbon neutrality. For this reason, in this paper, we explore the concept of net-zero energy houses (ZEHs)—houses designed to have an annual net energy consumption around zero—by presenting a constrained optimization problem to find the optimal number of photovoltaic panels and the optimal size of the battery system for home integration. Solving this constrained optimization problem is difficult due to its nonconvex constraints. Nevertheless, by applying a series of transformations, we reveal that it is possible to find an equivalent linear programming (LP) problem which is computationally tractable. The attainment of ZEH can be tackled by introducing a single constraint in the optimization problem. Additionally, we propose a sharing economy approach to the investment problem, offering a strategy that could potentially reduce investment costs and facilitate the attainment of ZEH more efficiently. Finally, we apply the proposed frameworks to a neighborhood in Japan as a case study, demonstrating the potential for long-term ZEH attainment. The results show that, under the right incentive, users can achieve ZEH, reduce their electricity costs and have a minimal impact on the main grid.

We recently established an impedance standard for the D-band, one of the 6G candidate frequencies. However, primary standards are difficult to use for routine calibration because they require multiple impedance standards and a lot of time to calibrate the vector network analyzer (VNA). Therefore, a transfer standard is needed to efficiently apply the calibration value and to propagate the uncertainty of the primary standard to a device under test (DUT). In this paper, we describe a design method for a transfer standard with small uncertainty even when an arbitrary DUT is measured. This is achieved by propagating the uncertainty of the primary standard to the transfer standard and then propagating it again to the uncertainty of the DUT. We developed a calibration kit that has low uncertainty over a wide frequency band, from 110 GHz to 170 GHz, and consists of waveguide offset shorts for ease of production. We also propose a method to minimize DUT uncertainty and a method to minimize “phase distance” to find the optimal length of the offset short. When using three offset shorts, an uncertainty similar to that of the short-open-load-thru (SOLT) calibration kit was obtained, and when using four offset shorts, an uncertainty comparable to the primary standard was obtained. Lastly, this paper examines the repeatability of measurements and reproducibility during the production process.

Reconfiguration of a distribution network is one of the main approaches to control and enhance distribution network indices, such as voltage profile and power losses. Distribution network operators perform reconfiguration for long-term or short-term periods based on network equipment and intended objectives. Long-term or static reconfiguration is suitable for traditional and modern networks with conventional switches. On the other hand, modern distribution networks that are equipped with one or more remote control switches can perform reconfigurations within short-term periods, to maximize predefined objectives. This paper presents a comprehensive review of recent literature on network reconfiguration. Reconfiguration methodologies are classified into five groups: classical methods, heuristic methods, metaheuristic methods, hybrid methods, and methods based on machine learning. The paper provides a general definition and comparison of the categories and discusses their application in dynamic and static reconfiguration. The paper introduces dynamic reconfiguration as the future challenges in smart and modern distribution networks and for the first time categorizes various methodologies in dynamic reconfiguration. The paper serves as a guide to assist engineers and researchers in selecting the most suitable methodology based on their system equipment and objectives.

The purpose of this paper is to review the scientific literature on direct load control (DLC) technology in the context of a smart grid (SG). This study synthesized and compared various articles from the literature with the aim of conducting a comprehensive analysis. This study aims to provide a detailed understanding of the landscape, recent advancements, and the relationships among various aspects within this field. The evaluation is conducted through a systematic literature review, examining recent advancements in the field. A survey of over 200 papers published between 1983 and 2022 is classified based on appliance information, aim/objective, constraint, management method and tool, implementation, and publication status. In the DLC application, the study identifies the type and population of appliances used in and the aim/objective and constraint considered. It also determines whether forecast, prediction, and estimation structures, optimization and control algorithms, and specific structures are employed. The practical aspect of the study examines various perspectives, including the modeling and presentation of the research. This involves identifying the validation methods, time period for implementation, solver, program, software, toolbox, and platform structures used during validation. Additionally, the characteristics of the database used for implementation and the location of the implementation are detailed. The publication year and the countries of the authors are also provided. Comparative relationships between these elements are presented based on the aforementioned information.

The use of Unmanned Aerial Vehicles (UAVs) has become popular in recent years, especially for their potential in various practical applications, but for their use to become a reality in this context, it is necessary to study about it. One of the main problems involving UAVs, regardless of the application to which it will be used, is path planning, which is crucial to ensure safety, economy, and effectiveness. In this study we present a literature review on the path planning optimization problem and the methods used to solve it. To this end, we seek to explore the existing papers in literature on this topic, identifying mathematical models, analyzing characteristics of the objective function, types of obstacles, number of UAVs considered, the nature of the solution adopted and deployments and integration in the Internet of Drones (IoD). A comparative analysis of the works analyzed was presented in the form of tables for each path planning technique considered. In addition, some advantages and safety of the methods were also listed. We furthermore present a set of open research challenges, high-level insights, and future research directions related to the UAV path planning problem in the context of IoD. This study contributes deeply with the advancement of state of art regarding the path planning strategies on the Internet of Drones since we provide a thorough analysis of characteristics of the mathematical models used in the UAV path planning problem reviewing papers published in relevant journals and conferences in the last 4 years (2018 to 2022), highlighting the advantages and disadvantages of each method as well as the possibilities of implementation and integration with IoD.

Data scientists in the Natural Language Processing (NLP) field confront the challenge of reconciling the necessity for data-centric analyses with the imperative to safeguard sensitive information, all while managing the substantial costs linked to the collection process of training data. In a Federated Learning (FL) system, these challenges can be alleviated by the training of a global model, eliminating the need to centralize sensitive data of clients. However, distributed NLP data is usually Non-Independent and Identically Distributed (Non-IID), which leads to poorer generalizability of the global model when trained with Federated Averaging (FedAvg). Recently proposed extensions to FedAvg promise to improve the global model performance on Non-IID data. Yet, such advanced FL algorithms trained on multilingual Non-IID texts have not been studied in industry and academia in detail. This paper compares, for the first time, the FL algorithms: FedAvg, FedAvgM, FedYogi, FedAdam and FedAdagrad for a binary text classification task using 12078 tailored real-world news reports in English, Portuguese, Spanish and Hindi. For this objective, pre-trained DistilBERT and BERT models fine-tuned with these texts are used. The paper results show that FedYogi is the most stable and robust FL algorithm when DistilBERT is used, achieving an average macro F1 score of 0.7789 for IID and 0.7755 for Non-IID protest news. The study also exhibits that BERT models trained with weighted FedAvg and FedAvgM can achieve a similar prediction power as centralized language models, demonstrating the potential of leveraging FL in the NLP domain without the need to collect data centrally.

In the realm of data-driven systems, understanding and controlling biases in datasets emerges as a critical challenge. These biases, defined in this study as systematic discrepancies, have the potential to skew algorithmic outcomes and even compromise data privacy. Mutual information serves as a key tool in the analysis, discerning both direct and indirect relationships between variables. Utilizing structural equation modeling, this paper introduces a synthetic dataset generation method founded on a two-step optimization algorithm that aims to fine-tune variable relationships and achieve targeted mutual information levels between attribute pairs. The algorithm’s first phase utilizes gradient-less optimization, focusing on individual variables. The subsequent phase harnesses gradient-based methods to unravel deeper variable interdependencies. The approach is dual-purpose: it refines existing datasets for bias mitigation and creates synthetic datasets with defined bias levels, addressing a crucial research gap. Two case studies showcase the methodology. One emphasizes the finesse of network parameter adjustments in a simulated setting. The other applies the methodology to a realistic job hiring dataset, effectively reducing bias while safeguarding key variable relationships. In summary, this paper offers a novel method for bias management, presents tools for quantitative bias adjustments, and provides evidence of the method’s broad applicability through varied use cases.

This paper analyzes the OBL strategy’s impact on optimizing the GWO algorithm and identifies three shortcomings. the specific limitations of the OBL optimization approach. To address these three shortcomings and enhance both global optimization and local exploration capabilities of GWO, this paper introduces a follow-controlled opposition learning strategy. then, the paper analyzes the control parameter C of the grey wolf algorithm to investigate its impact on global optimization and local exploration. Based on these properties, a new control parameter C is proposed. The proposed learning strategy and control parameter C are introduced into the traditional grey wolf algorithm to obtain the FCGWO algorithm. Finally, this paper conducts a comparative analysis of the FCGWO algorithm in comparison to other meta-heuristic algorithms, as well as the enhanced grey wolf algorithm, utilizing 23 benchmark test functions and 2 engineering problems. The results indicate that FCGWO effectively avoids the shortcomings of the traditional OBL, while also outperforming other algorithms significantly in terms of solution quality.

This paper proposes an innovative framework of a parameter estimation procedure based on the well-established relay-feedback experiment paradigm. The novelty consists in consideration of asymmetric dynamics and non-equal static gains of the identified system. A different system behavior after changing the input variable polarity near the operating point is rarely considered or even omitted within relay-based parameter identification tests, in contrast to the common use of asymmetry in the nonlinear relay element. The thing is that many existing relay-based identification techniques in the frequency domain use integrations, assuming that the system output operating point coincides with the setpoint value (i.e., the offset between them is zero). However, this is not true for asymmetric dynamic systems, which yields considerably erroneous parameter estimation as the integration result is highly sensitive to the baseline value. The resulting iterative numerical optimization-based algorithm is built-up using a chain of natural assumptions and step-by-step thought experiments. The proposed framework is applied to the well-established exponential decaying method in this paper. Some computation aspects of the algorithm are discussed. A comparative numerical study illustrates the efficacy of the proposed strategy, where several frequency-fitting-based and descriptive-function-based competitive approaches are considered.

The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.

To overcome the problem of large UWB positioning errors caused by obstacles and other factors in the complex environment of substations, a positioning method combining UWB and virtual-real scene similarity measures is proposed, which effectively improves the positioning accuracy of substation operation and maintenance personnel. To further improve the positioning accuracy of the operation and maintenance personnel in the complex environment of the substation, and reduce the positioning time, this paper analyzes the influence of the virtual scene image acquisition step (x, y, yaw, pitch) on the positioning error. Firstly, the mathematical relationship between the step size parameter and the positioning error as well as the positioning time is quantified using the spatial geometric relationship and probability error theory, and the spatial error model of the fusion positioning algorithm is established; then, based on the established spatial error model, the acquisition step size and the positioning error and positioning time have been optimized to make the positioning time the shortest under the conditions of allowable positioning error. The experimental results show that the spatial error model established in this paper has high accuracy and the optimization results are reliable; the spatial error model is used to optimize the acquisition step parameters, which can ensure the UWB and virtual-real scenes similarity measurement fusion positioning algorithm with positioning error less than 0.2 m (with confidence greater than 0.95) and positioning time of 0.173 s, which significantly improves the positioning speed.

Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates—the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people*week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people*week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%~2.75% people*week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people*week infections and avoid vaccine waste when more vaccines are available.

Growing electricity demand, the deployment of renewable energy sources and the widespread use of smart home appliances provide new opportunities for home energy management systems (HEMSs), which can be defined as systems that improve the overall energy production and consumption of residential buildings by controlling and scheduling the use of household equipment. By saving energy, reducing residential electricity costs, optimizing the utilization rate and reliability of utility companies’ power systems, and reducing air pollution for society, HEMSs lead to an enhancement in the socioeconomic development of low-carbon economies. This review aims to systematically analyze and summarize the development trends and challenges of HEMSs in recent years. This paper reviews the development history of the HEMS architecture and discusses the characteristics of several major communication technologies in the current HEMS infrastructure. In addition, the common objectives and constraints related to scheduling optimization are classified, and several optimization methods in the literature, including various intelligent algorithms, have been introduced, compared, and critically analyzed. Furthermore, experimental studies and challenges in the real world are also summarized and recommendations are given. This paper reveals the trend from simple to complex in the architecture and functionality of HEMSs, discusses the challenges for future improvements in modeling and scheduling, and shows the development of various modeling and scheduling methods. Based on this review, researchers can gain a comprehensive understanding of current research trends in HEMSs and open up ideas for developing new modeling and scheduling approaches by gaining insight into the trade-offs between optimum solutions and computational complexity.

The progress in computational offloading is heavily pushing the development of the modern Information and Communications Technology domain. The growth in resource-constrained Internet of Things devices demands the development of new computational offloading strategies to be sustainably integrated in beyond 5G networks. One of the solutions to said demand is enabling Mobile Edge Computing (MEC) powered by advanced methods of Machine Learning (ML). This paper proposes the application of ML-powered computational offloading strategy in a wireless cellular network by applying the traditional fundamental Travelling Salesman Problem (TSP) on computational offloading location selection. The main specificity of the proposed approach is the use of imagery data. Thus, the paper executes a literature review to identify existing strategies. It further proposes a novel method utilizing the location-like imagery data to identify the most suitable computational location by executing the search for an identified route between locations using the proposed Deep Learning (DL) model. The model was evaluated and achieved MAE – 1,575, MSA – 10,119,205, $R^{2}$ – 0.98 on the testing dataset, which outperforms or is comparable with other well-known architectures. Moreover, the training time is proven to be 2–10 times faster. Interestingly, the MAE values are relatively low compared to the target values that should be predicted (despite rather high MSE results), which is confirmed by the almost perfect $R^{2}$ value. It is concluded that the proposed neural network can predict the target values, and this solution can be applied to real-world tasks.

In this paper, a new problem of reverse image filtering is addressed. The problem is to reverse the effect of an image filter given the observation $\boldsymbol {b}=g(\boldsymbol {x})$ . The filter $g$ is modelled as an available black box. An inverse method is proposed to recover the input image $\boldsymbol {x}$ . The key idea is to formulate this inverse problem as minimizing a local patch-based cost function. To use gradient descent for the minimization, total derivative is used to approximate the unknown gradient. This paper presents a study of factors affecting the convergence and quality of the output in the Fourier domain when the filter is linear. It discusses the convergence property for nonlinear filters by using contraction mapping as a tool. It also presents applications of the accelerated gradient descent algorithms to three gradient-free reverse filters, including the one proposed in this paper. Results from extensive experiments are used to evaluate the complexity and effectiveness of the proposed algorithm. The proposed algorithm outperforms the state-of-the-art in two aspects. (1) It is at the same level of complexity as that of the fastest reverse filter, but it can reverse a larger number of filters. (2) It can reverse the same list of filters as that of a very complex reverse filter, but its complexity is much lower.

Control systems are widely used in our lives, and good control can be achieved by obtaining the optimal tuning parameters of the control system. The number of parameters that need to be adjusted for different control systems varies. With an increase in tuning parameters, the difficulty of tuning grows. Therefore, this paper proposes an improved monkey multiagent DRL (IMM-MADRL) algorithm and selects 3 test functions to test the setting environment of 2–7 parameters. Thus, these parameters are adjusted. The IMM-MADRL algorithm is based on the modified monkey-multiagent DRL (MM-MADRL) algorithm, and its initialization method, position update method and somersault operation are further improved so that it can perform good parameter tuning for a control system with many parameters. The simulation part of this paper proves the advantage of the IMM-MADRL algorithm in a multiparameter control system.

Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

The discussion context of this paper is big data processing of MapReduce by volunteer computing in dynamic and opportunistic environments. This paper conducts a series of simulations to explore the relationship between the overall performance of volunteer overlays responding to different workload of big data problems. The discovery from the simulations includes some optimization points in overlay size, going over which by adding more volunteers brings little benefit for the overall performance. Based on the discovery of optimization points, this paper proposes a bootstrapping protocol, which can adapt volunteers into variable-sizes overlays, enabling workflow of single-round MapReduce or multiple-round MapReduce, and a single or multiple overlays for each round. The variable overlays aim to create adaptive workflow during MapReduce processing, so that the optimization points can be caught. As another benefit, the unnecessary computing-capacities can be released during computing when the optimization points are reached. The case study shows a few optimization workflows that are formed by the proposed bootstrapping protocol to process the big data cases. The workflows lead to the optimization points and dynamically balance the workload at the same time. The experiment results have demonstrated that the optimization strategies have either achieved 36% or 71% higher performance than the plain MapReduce workflow and minimized the use of computing resources by releasing 12.5% to 75% volunteers during computing, where the original plain MapReduce must hold all the volunteers to the end of computing. The extensibility of the simulation parameterization to more diverse real-world applications have been clarified.

Creating the schedule for an academic conference is a time-consuming task. A typical conference schedule consists of sessions containing papers addressing the same research topic. To construct a schedule, conference papers must be grouped according to their research topic, and the obtained groups should fit the assigned time slots. This paper proposes an approach to automating the schedule-creation process. We use multilingual, neuro-symbolic paper representations and novel constrained clustering to group papers into clusters of predetermined size with the same topic fitting the schedule structure. In the process, we combine machine-learning, natural language processing, network analysis, and combinatorial optimization. We tested the components of the proposed approach on a newly created database of papers from six machine learning conferences, which were manually labeled by their research topics. The entire system was tested on two real-world conferences in a multilingual setting. The developed methodology is incorporated into an interactive automatic conference-scheduling system NeSyChair (Neuro-Symbolic Conference Chair), which can be used to create and improve conference schedules.

Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learning-based approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

This paper presents an optimization methodology for inductorless noise-cancelling CMOS Low-Noise Amplifiers (LNA), whose performance typically depends on a tight balance in the design of two transistor stages. Due to the different functions of the two parts, noise-cancelling amplifiers become very difficult to analyze in detail by closed-form expressions or straight simulations: each section significantly affects the results of the other. In addition, opposed specifications, such as gain and cut-off frequency, suppose another grade of complexity due to the interplay of the two branches of the circuit. As a solution, the proposed methodology uses a visualization of the design window in 2-dimensional space to optimize the different parameters of the specifications without compromising the others. All specification constraints are represented in a single figure instead of one graph per parameter. Compared with most optimization methods, the design window methodology observes the design span instead of isolated design points that might not guarantee feasibility. Furthermore, as a simulation-driven exploration method, it benefits from complete device models with high-order effects that would be too complex to include in analytical expressions but critical to achieving maximum efficiency. As an example of the method, the paper describes the optimization of the well-known CS-CG noise-cancelling LNA in 65-nm standard CMOS technology. Final post-layout simulations report very competitive results with a 3.7-dB noise figure, a 17-dB gain, and a cut-off frequency above 7 GHz.

Massive multiple-input multiple output (MIMO) systems have been introduced as a resolution for next generation cellular systems. The complexity of computing the precoding in massive MIMO is increased. So, studying a scalable precoding in massive MIMO system is a challenge task. In this paper, we propose a scalable precoder based polynomial for multiuser massive MIMO system, where base station (BS) is equipped with antennas that simultaneously communicate user equipments (UEs). This precoder applies matrix polynomial instead of matrix inversion. An energy efficiency (EE) optimization problem is formulated. This paper also studies optimal design parameters, which are the optimal transmit power, active UEs and number of antennas at BS. Mathematical formula for the EE-maximizing parameter estimations was mathematically analyzed with different orders of polynomial precoder. The impact of increasing polynomial orders is studied on the system performance. Comparison between proposed precoding technique and conventional techniques (i.e., zero forcing (ZF) precoder, minimum mean square error (MMSE) precoder and linear precoder) is provided. Results have shown that maximal EE and area throughput are achieved by deploying polynomial precoder in multiuser massive MIMO system. It can achieve better performance compared with conventional techniques. Utilization of polynomial precoder enhances the performance and provides high EE values.

This paper faces the energy management problem of cooperative microgrids in a smart energy district. In particular, the aim of the research work is to propose an innovative optimization model to solve the problem of energy management in a district composed of several microgrids, taking into account uncertainties of key parameters. In this context, the objective of the paper is threefold: i) maximize the use of energy purchased at the day-ahead market; ii) minimize the need of additional and expensive energy in real-time iii) optimize the integration of renewable energy sources (RES), energy storage systems (ESS) and electric vehicle (EV) batteries in the microgrid. To these goals, the District Energy Management System (DEMS), i.e. the central controller of the district, must balance the microgrids energy demand with the optimal integration of RES, ESS and the batteries of EVs that are seen as movable storage devices shared among buildings. Moreover, the energy surplus can be sold back to the main power grid. The DEMS problem is solved by two approaches. In the first approach, the energy demand, the RES production and the costs are known and a linear programming problem is formalized and solved by the DEMS. In addition, a second approach is proposed in order to address the parameters uncertainty and is formalized as a stochastic linear programming problem. The optimization problems solutions provide the optimal strategy to schedule the charging and discharging operations of the storage systems and the electric vehicle batteries. A simulated case study demonstrates the benefits of the proposed approaches for the smart district.

Non-deferrable loads equipped with distributed energy resources, such as solar photovoltaic generation and energy storage, can offer a wide range of benefits to the power system, including load leveling, hedging against forecast uncertainty, and ancillary services. This paper presents a techno-economic approach to optimally equip a flexible energy resource, namely a battery energy storage system (BESS), with non-deferrable loads for capacity enhancement of a distribution grid. The proposed approach is generic in applicability, and includes two different optimization mathematical models. The first model quantifies the needed flexibility with non-deferrable loads, based on a consideration of distribution grid limitations, while the second model determines the optimal flexibility amount associated with non-deferrable loads considering investor profitability. Thereafter, a distribution operations model is developed to evaluate the impact of non-deferrable loads, equipped with an optimal size of BESS, on the distribution system loading capacity. An example of non-deferrable loads considered in this paper is that of the rapid charging loads of plug-in electric vehicles (PEVs) that cannot be controlled or shifted due to the short stay of PEVs at a charging facility. Hence, a probabilistic model for PEV rapid charging loads is first developed. Case studies and simulation findings, considering a 32-bus distribution system, demonstrate that the proposed approach helps reduces the impact of non-deferrable loads on a distribution system and enhances the system power margin, which defers the need for system upgrades.

The BaSIC(DMM) topology has been experimentally demonstrated to improve the short-circuit time for a 1.2 kV SiC power MOSFET product from $4.8~\mu \text{s}$ to $7.9~\mu \text{s}$ with a 17% increase in on-state resistance by utilizing a commercially available 100 V rated Gate-Source-Shorted (GSS) Si Depletion-Mode power MOSFET (DMM). The optimization of the Si GSS-DMM is discussed in this paper to achieve even superior performance, namely larger short-circuit time with less increase in on-resistance. It is theoretically demonstrated for the first time that a highly desirable short-circuit time of $10~\mu \text{s}$ , similar to Si IGBTs, can be achieved for two SiC power MOSFET products with less than 3% increase in on-resistance. This was accomplished by reducing the breakdown voltage rating of the Si GSS-DMM from 100 V to 30 V, altering the cell design parameters, and utilizing the trench-gate design. The theoretical analysis provided in this paper provides valuable design guidelines for manufacturers of Si GSS-DMM devices to achieve optimum performance for use in the BaSIC(DMM) topology.

Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. In this context, this paper visits one particular direction of interplay between learning-driven solutions and optimization, and further explicates the subject matter with a clear background and summarized theory. For instance, machine learning and its offsprings are trending because of their enhanced capabilities in automating analytical modeling. In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training. This paper overviews how machine learning-based techniques, namely deep neural networks, echo-state networks, reinforcement learning, and federated learning, can be used to solve complex and analytically intractable optimization problems, for which specific cases are examined in this paper. The paper particularly overviews when learning-based algorithms are useful at solving particular optimizing problems, especially those of random, dynamic, and mathematically complex nature. The paper then illustrates such applications by presenting particular use-cases in communications and signal processing including wireless scheduling, wireless offloading and resource management, power control, aerial base station placement, virtual reality, and vehicular networks. Lastly, the paper sheds light on some future research directions, where the dynamicity and randomness of the underlying optimization problems make deep learning-driven techniques a necessity, namely in sensing at the terahertz (THz) bands, cellular vehicle-to-everything, 6G communication networks, underwater optical networks, distributed optimization, and applications of emerging learning-based techniques.

This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units’ costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units’ structure and characteristics, RESs’ correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.

The purpose of microgrids is to improve system flexibility and resilience during normal and emergency conditions. The ceaseless load growth mandates to increase microgrid’s capacity, thereby improving the system flexibility and resilience. However, capacity expansion requires significant investments, making it essential to identify the optimal capacity of energy resources. The methodologies proposed in the literature identifies the microgrid’s capacity with an assumption of investments with a single installment. This way of theoretical approach leads to unrealistic solutions. Besides, microgrid’s participation in a flexible market will enhance its performance both in commercial and technical aspects. Therefore, this paper proposes a realistic framework with the concept “expansion through time” inspired by “Real Options Theory.” This framework includes practical parameters like resource & load uncertainty, physical space required to install, revenue generated by resources, and maximum demand penalty, on top of electrical parameters; constrained with significant return in investments to improve the overall savings. In addition, this paper proposes a market participation model for microgrid, which defines a bidding process with two components, such as regular and flexible portions under both normal and extreme conditions. This study considers renewable-based energy resources like solar-photovoltaic plants (SPPs) and battery energy storage systems (BESSs) as microgrids’ energy resources. The system chosen for testing the efficacy of the proposed framework is a real-world active-microgrid of Malta College of Arts, Science and Technology (MCAST), located on an island.

Optical wireless networks realized by means of gigabit optical wireless communication (OWC) systems are becoming, in a variety of applications, an important alternative, or a complementary solution, to their fiber-based counterparts. However, performance of the OWC systems can be considerably degraded in periods of unfavorable weather conditions, such as heavy fog, which temporarily reduce the effective capacity of the network. In this paper, we focus on optical wireless mesh networks that use terrestrial links (called FSO – free-space optical – links) composed of several parallel full-duplex FSO systems, and present two complementary solutions that together provide a means to maximize network traffic performance in various weather conditions encountered during network operation. The first solution is a method for estimating the degradation of the effective FSO link capacity in adverse weather conditions such as fog, rain and snow (called the weather states in this paper). The second solution is an optimization model aiming at maximizing the network traffic throughput for a given list of weather states, derived from the conducted measurements. The model assumes the so-called affine flow thinning (AFT) traffic routing and protection mechanism capable of controlling the end-to-end traffic flows in response to fluctuations of capacity available on FSO links caused by changing weather conditions. The proposed link capacity modeling approach and the elaborated optimization model are verified through an exhaustive numerical study, illustrating the trade-off between the increase of traffic performance of the FSO networks and the corresponding cost of additional OWC systems.

At present, most logistics systems, especially those dedicated to e-commerce, are based on artificial intelligence techniques to offer better services and increase outcomes. However, the variety and complexity of resource allocation, as well as task scheduling, denote that dynamic environments have still great challenges to overcome. So advanced models based on strong algorithms are required. Introducing advanced models into scheduling solutions is a promising way to enhance logistics efficiency. As a result, managing system resources remain essential to optimize task scheduling respecting the interactive impacts, and logistics systems requirements. In response to these challenges, in this paper, a powerful solution based on a Long short-term memory (LSTM) model is proposed to optimize resource allocation and to enhance task scheduling in a smart logistics framework. This paper explores some of the most important scheduling techniques and hypothesizes that deep learning techniques might be able to afford accurate approaches. The proposed smart logistics model lays on strong techniques, for that, experimental simulations were conducted using various project instances. The validation tests demonstrated competitive results with important performance rates i.e.: accuracy of 92,44% with a precision of 93,83, a recall of 95.18%, F1-score of 94,92%, and an AUC of 88,17%. These results reveal the proof-of-principle for using LSTM models for effective and truthful logistics operations.

With the expansion of the scale of subsea oil and gas resource exploitation, the offshore multiplatform interconnected power systems have become the main form of offshore platform group energy supply. The offshore multiplatform interconnected power system with wind power can effectively reduce the system fuel consumption and carbon emission, which has become an important direction for the development of future offshore multiplatform interconnected power systems. In this paper, the scenario method is adopted to portray the wind power output. System evaluation indicators considering the economy, reliability and environmental benefits of the system are proposed. Considering the nonnegligible impact of the cable size selection problem on the system economic cost, this paper introduces cable size selection variables in the expansion planning model of the offshore multiplatform interconnected power system with wind power to achieve accurate optimization of the system planning scheme but also greatly increases the difficulty of solving the model. A model linearization method is applied to linearize the nonlinear part of the model, so that the optimized model is a mixed-integer linear programming model under the determined system topology, and thus can be solved using commercial planning software. In addition, an improved genetic algorithm is proposed to optimize the system topology, which improves the efficiency of the model solution. The cases of different scale verify the validity of the model and the solution algorithm and show the impact of the maximum penetration rate of wind power on the total system cost.

This paper proposes a decision support framework for defining planning parameters for national crop production. The proposed framework addresses the gaps in policymaking, the role of all stakeholders, and uses historical data of crop production in different sectors of land in optimizing the profits that shall meet the defined constraints including national requirement, and export demand of different crops. There are many agro-economy-based countries where agriculture is the main contributor to their GDP, while there are others who always struggle to meet their national need while optimizing the agro-economy component of their earnings. Crops are not always produced with keeping the demand and production balance. Rather, the crops are mostly produced according to the farmer’s ease and last year’s prices for profit maximization leading to excessive production of one type of crop, lowering of profit margins, and shortage of some key crops for national need. This paper presents a decision aiding tool that can be used for farmer’s awareness for crop production planning that meets the national and export needs while maximizing farmer’s earnings. The model is tested on historical data of different segments of cultivated lands in Pakistan for validation, with the first experiment performed on single farmland with multi-fields while the second performed for multi-locations and multi-fields. The results are promising and provide estimates of net profit, expected production against the demand for each crop, and analysis that aids in crop planning, before and after the application of our model while meeting all necessary constraints.

Many studies have been proposed to prove that technical analysis can help investors make trading decisions. The moving average (MA) is a widely used technical indicator that plays an important role in this field since it directly reflects stock fluctuations. However, most studies ignore the parameter settings of the MA, which leads to underestimation of the potential of the MA. Therefore, this paper is the first attempt to remove all restrictions and extend the limits of the MA to take advantage of the MA’s capability well. It also uses different kinds of MA, such as the weighted moving average (WMA) and the exponential moving average (EMA), to compose trading strategies. Our system proposes the global best-guided quantum-inspired tabu search algorithm (GQTS), which is better at searching than traditional algorithms, to optimize trading strategies based on the MA. Furthermore, an innovative 2-phase sliding window is invented to consider more investment situations in changeable stock markets. In summary, this paper intended to investigate the ability of MA and proposed dynamic and intelligent trading strategies based on MA, GQTS, and 2-phase sliding window to assist investors to make trading decisions. The experiments show that the proposed system flexibly discovers better trading points. Our system outperforms traditional methods and beats the buy-and-hold strategy to yield significant profits both in developed and emerging stock markets.

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

Life threatening nature of cancer and toxic effects of chemotherapy demand for an optimal design of treatment protocol. The main objective of treatment design is to maintain adequate health of patient while administering a continuous chemo dose for effective decimation of cancer. Mathematical model adopted in this paper is first order nonlinear coupled ordinary differential equation (NCODE) relating tumor, effector immune and normal cells under effect of chemotherapy. This paper primarily utilizes the Bernstein polynomial with genetic algorithm based coefficient tuning for solution of the tumor model. Secondarily sliding mode controller (SMC) is used as optimal control for normal and immune cells boosting in addition to escalated tumor minimization. The hybrid approach used in this research produces a potent minimization of cancer. Application of SMC ensures normal cells concentration well above the critical threshold; hence a continuous treatment dose is viable. Proposed methodology enhances the effect of chemotherapy over cancer while maintaining healthy state of patient.

The present paper presents a newly designed dexterous mobile parallel mechanism for fusion reactor vacuum vessel assembly, the robot system has advantages in terms of compact design, the capability to carry out heavy-duty machining tasks, evacuation, and has less space occupation compared to other robot systems in existence. Despite different robot systems are studied in the fusion reactor, there is still a lack of research on mechanism development for vacuum vessel assembly, which is attractive to future fusion reactors. In the fusion reactor, the robot systems will carry out different tasks, such as welding and machining. The assembly tasks of the vacuum vessel will be performed from inside of the vacuum vessel on-site. Then the paper introduces the single-objective and multi-objective optimization design of the proposed mechanism, the optimized objective is considered to be a combination of parallel mechanism dynamic machining force, dexterity, stiffness, and workspace volume. The design variables are derived from the geometry of the fixed and movable platforms, which include mass, inertia, the sizes of the platforms, and distances between universal joints located on the platforms. In the multi-objective optimization, non-dominated sorting genetic algorithm II is adopted and different trajectories are designed to simulate the machining process, which further turns the local optimization problem into a global optimization problem. Finally, the optimized results are extracted and analyzed. Simulation results indicate the effectiveness of the proposed multi-objective optimization approaches and multi-objective optimization is found to be more reliable than single-objective optimization.

Agroalimentary laboratories typically process samples that require different types of analysis depending on the substance being analyzed and the requirements of the clients. A key parameter for client satisfaction is the time that it takes since the samples arrive at the laboratory facilities and the results are provided to the client. Thus, the order in which the different samples are processed and the analysis are performed can have a significant impact in the overall customer satisfaction. This paper proposes a novel approach for planning the production of agroalimentary laboratories based on maximizing a measure of customer satisfaction derived from this lag between sample reception and analysis finalization. This way, a planning model is defined which, based on the basic version of the Resource-Constrained Project Scheduling Problem, can be used to optimize an objective function that focuses on the client satisfaction, considering the relative relevance of clients and samples. The paper includes a detailed presentation of the parameters, variables and constraints required to define the problem, along with the corresponding modeling assumptions. The applicability of the approach is presented with a discussion of the solutions provided by the optimization problem for a set of scenarios of interest, which show the suitability of the method as a systematic tool for planning the operations of agroalimentary laboratories.

Web services have become a standard way to provide functions of information systems. The number of web services grows rapidly with the increasing popularity of microservices architecture. In consequence, many business processes are executed entirely through web services. Therefore, optimizing the performance of business process execution may bring many benefits. There are many optimization methods in this area. Our systematic literature review aims to introduce available methods to researchers interested in the optimization of business process execution. We queried four databases: ACM, IEEE Xplore, Science Direct, and Springer. Out of 12150 initially found papers, we have selected 128 for the review. We have grouped methods presented in those papers into three stages of business process optimization: Resource Allocation, Service Composition, and Service Scheduling. Service Composition attracts the largest group of researchers with a vast majority of 119 articles in it. Moreover, the most popular are genetic algorithms. In general, researchers mainly propose heuristic methods that optimize business processes during run-time. We see the potential for further exploration at both Resource Allocation and Service Scheduling stages.

Resource allocation strategies for simultaneous wireless information and power transfer (SWIPT) employing orthogonal frequency division multiplexing (OFDM) typically rely on subcarrier assignment for multiple access. For information transfer, however, subcarrier assignment is only sum-rate optimal when the network is interference limited; otherwise, it is optimal to reuse subcarriers. Beamforming-based space-division multiple access (SDMA) is a multiple access technique that enables subcarrier reuse by managing interference. Although beamforming is considered a key SWIPT component, OFDM-based SWIPT has yet to utilize beamforming for SDMA. As such, this paper seeks to develop a low-complexity resource allocation strategy to jointly allocate power and design beamforming vectors for an OFDM-based multiuser (MU) multiple-input single-output (MISO) broadcast channel (BC) SWIPT system capable of utilizing SDMA. To this end, a non-convex optimization problem is formulated in which a weighted sum of user data rates is maximized subject to meeting receive and transmit power constraints. The time-sharing property of multicarrier systems is observed to justify utilizing Lagrangian decomposition despite the non-convexity. Whereas previous SWIPT research used Lagrangian decomposition to simplify subcarrier assignment, this paper instead uses it to enable SDMA through beamforming. Beamforming is achieved via a medium access channel (MAC)-BC duality, which enables a closed-form solution for the beamforming vectors given the dual MAC power allocation. The dual MAC power allocation is approximated using a successive convex approximation (SCA) technique. The result is a resource allocation algorithm that provides close-to-optimal performance with low computational complexity.

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

Most traditional software reliability growth models (SRGMs) assume immediate fault correction upon detection and therefore only consider fault detection process (FDP). In order to be more realistic, some researchers have tried to incorporate fault correction process (FCP) and fault introduction process (FIP) into the software reliability models. However, it is still difficult to incorporate into the analytical software reliability models some other factors, such as the different fault detection and correction capabilities of debuggers. In this paper, a simulation approach is proposed to model FDP, FIP, and FCP together considering debuggers with different contributions to fault detection rate, different fault correction rate and different fault introduction rate. Besides, this paper also constructed a cost calculation method to optimize the testing design including debuggers assignment and software release time. Some numerical examples are provided to illustrate the proposed model. The results show that the trends of FDP, FCP and FIP are consistent with the intuition to the practice of software testing, and the optimal testing resources allocation and the optimal release time can be obtained according to the proposed model.