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Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]

• A review of waste management optimization models (WM-OMs). 2. • A novel characterization method for examining and benchmarking the features WM-OMs. 3. • A pioneering guide for the selection of proper uncertainty analysis approaches. 4. • A compilation of characteristics of the papers reviewed and their data (Appendix). 5. • A perspective on the design of WM-OMs for different applications and stakeholders. 6. The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders. [ABSTRACT FROM AUTHOR]

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

Objective: Due to the alarming rise in COVID-19 cases every day in India, numerous academics are already developing a variety of math-based estimation models to forecast the pandemic future course. This paper makes use of publicly available data to anticipate certain COVID-19 trajectories in India. Methods: We used the Auto-Regressive Integrated Moving Average Model, a time series model, to anticipate the number of COVID-19 infected cases every day soon. People need to consume nutritious food that is well-balanced and contains the right number of calories, nutrients, as well as vitamins for strong development, bearing in mind that sustaining and repairing bodily tissues is the goal while preventing unfavorable illnesses and disease. Results: Recent studies have shown that healthy eating can help lower the possibilities of developing cancer, cardiovascular disease and for COVID patients. Therefore, an optimization strategy and LINDO software was used to solve the model. This study offers an exhaustive daily meal plan created especially for hospital patients, acting as a helpful resource for school administrators. With six different food choices, the diet plan makes sure that you get all the nutrients you need each day at a reasonable price. Conclusions: The model is solved using LINDO software in the study, which shows how effective it is when compared to other heuristic techniques like biological algorithms. It is determined through thorough investigation that the chosen meals are both financially and nutritionally feasible to serve in ho spital settings. Hospital patients make up the study's participants, and each day's total cost comes to Rs109.34. This cost makes it possible to deliver meals that are minimally more costly but of higher quality, improving the patients' overall nutritional value. [ABSTRACT FROM AUTHOR]

The present paper provides a comparison of different optimization techniques applied to the model-based design of a Buck Zero Voltage Switching (ZVS) Quasi-Resonant DC-DC Converter. The comparison was made both on the basis of the duration of the optimization procedures and in terms of guaranteeing the performance of the power electronic device. The main task of the paper is to present various techniques based on the use of mathematical software for the optimal design of Quasi-Resonant DC-DC converters. These topologies were chosen because in them, the design is carried out according to computational procedures, in which several iterations are often necessary for the successful completion of the process. An optimization procedure with a target function reference curve of the output voltage was used. In this way, the optimization is performed without the need for a complete design of the device but only by using base ratios, design constraints, and past experience to determine initial values and intervals of change in circuit parameters. This is also the main advantage of the used optimization of the reference curve type of the output, compared to applying other objective functions, such as achieving minimum losses or maximum efficiency of the device. [ABSTRACT FROM AUTHOR]

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future. [ABSTRACT FROM AUTHOR]

Tunicate Swarm Algorithm (TSA) is a new bio-based optimization technique that has proven not only to be able to compete with other methods but has also shown successful performance in classic design engineering problems/benchmark test problems. However, like some population-based methods, TSA tends to be trapped in local optima, converging to global optima in a long time, unbalanced exploitation/exploration, and the inability to effectively solve high-capacity engineering problems. In this paper, the M-TSA, which is a Modified version of the TSA, is proposed to overcome such problems. M-TSA was developed in three steps. The first is the new movement strategy that improves the movement of tunicates with a spiral movement, the second is the new herd strategy that improves the herd movement of tunics with the Levy movement, and the third is the consideration of the FAD effect. In this study, the efficiency and robustness of the M-TSA algorithm is tested on the CEC'17 test suite, six real-life design engineering problems, and two complex power system engineering problems. The test results were compared with other techniques reported in the literature and with the original TSA. Comparing the results from the M-TSA technique with other techniques proves the effectiveness of M-TSA with better exploration/exploitation balance and optimal solution finding. In this paper, MATLAB 2020b software is used for optimization problems simulation. [ABSTRACT FROM AUTHOR]

A multi-modal search space can be defined as having multiple attraction basins - each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers. [ABSTRACT FROM AUTHOR]

This paper optimizes the assignment of shared automated vehicles under users' uncertain departure times. Automated vehicles can drive themselves, so no staff are needed to relocate vehicles in the one-way carsharing system. To optimize fleet placement and use, a twophase solution method was established. Phase 1 strategically distributes vehicles across stations using a system optimization approach, while Phase 2 tracks vehicle movements via an agent-based simulation model. Phase 1 solutions serve as inputs to Phase 2 simulations. Using a fleet size of roughly 10,000 vehicles, case study applications were run across the Austin, Texas region's six-county network. In the base case setting, results suggest that system profits are optimized when vehicle rental is priced at $1.28=km ($0.8=mi). The number of proactive vehicle relocations falls 9.8% if the relocation operation cost rises from $0.096=km ($0.06=mi) to $0.32=km ($0.2=mi). Average per-trip profit is $10.60 when using high-cost vehicles, and $11.60 when using low-cost vehicles. Results from a 3-h simulation show an average person-trip length of 25 km (15.6 mi), with 29.6 min of average driving time. When a 24-h day was simulated, the vehicle-occupied time and vehicle-distance traveled were 4 h and 200 km (125 mi) per vehicle-day, respectively. The low coefficient of variation of satisfied demand across 30 demand scenarios suggests the robustness of the two-phase solution method. [ABSTRACT FROM AUTHOR]

The application of 'Target Cascading' in the product development process is well known to be effective in various industry fields, and it is a methodology that is already actively applied in various forms in the field of vehicle Ride & Handling (R&H) performance development. This paper proposes a model-based vehicle performance benchmarking methodology and a technology for setting system characteristics to achieve handling performance objectives as a means of utilization for this. Rather than introducing already well-known target deployment techniques, a process is proposed for practical use in the vehicle development phase through the development of interfaces between low degrees of freedom vehicle model and optimization software. The methodology presented in this study is expected to be available in a timely manner as a practical vehicle performance development tool. [ABSTRACT FROM AUTHOR]

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Many different aspects of software system development and verification rely on precise function identification in binary code. Recognition of the source Assembly functions in embedded systems is one of the fundamental challenges in binary program analysis. While numerous approaches assume that the functions are given a priori, correct identification of the functions in binaries remains a great issue. This contribution addresses the problem of uncertainty in binary code in identification of functions, which were optimized during compilation. This paper investigates the difference between debug and optimized functions via modeling of these functions. To do so, we introduce an extensible model-centred hands-on approach for examining similarities between binary functions. The main idea is to model each function using a set of predetermined, experimentally discovered features, and then find a suitable weight vector that could give impact factor to each such a feature. After finding the weight vector, the introduced models of such desired functions can be identified in binary software packages. It means that we reduce the similarity identification problem of the models to a classical version of optimization problems with one optimization criterion. Using our implementation, we found that the proposed approach works smoothly for functions, which contain at least ten Assembly instructions. Our tool guarantees success at a very high level. [ABSTRACT FROM AUTHOR]

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables. [ABSTRACT FROM AUTHOR]

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells. [ABSTRACT FROM AUTHOR]

• Variational Quantum Circuits (VQCs) play a key role in several applications. • VQCs are parameterized quantum circuits trained by using classical optimizers. • The paper proposes to apply memetic algorithms to train VQCs. • The designed memetic algorithm outperforms the state-of-the-art classical optimizers. Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session. [ABSTRACT FROM AUTHOR]

A positively invariant set is an important concept in dynamical systems. The study of positively invariant set conditions for discrete-time systems is one interesting topic in both theoretical studies and practical applications research. Different methods for characterizing the invariance of different types of sets have been established. For example, the ellipsoidal and the Lorenz cone, which are quadratic convex sets, have different properties from a polyhedral set. This paper presents an optimization method and a dual optimization method to characterize the positive invariance of the ellipsoidal and the Lorenz cone. The proposed methods are applicable to both linear and nonlinear discrete-time systems. Using nonlinear programming and an induced norm, the positive invariance condition problems are transformed into optimization problems, and the dual optimization method is also used to give equivalent dual forms. Fewer results on the positive invariance condition of Lorenz cones can be found than for the other type of set; this paper fulfills the results of this problem. In addition, the proposed methods in this paper provide more options for checking the positive invariance of quadratic convex sets from the perspective of optimization and dual optimization. The effectiveness of this method is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

Data-centric is a newly explored concept, where the attention is given to data optimization methodologies and techniques to improve model performance, rather than focusing on machine learning models and hyperparameter tunning. This paper suggests an effective data optimization methodology for optimizing imbalanced small datasets that improves machine learning model performance. This paper is focused on providing an effective solution when the number of observations is not enough to construct a machine learning model with high values of the estimated magnitudes. For example, the majority of the observations are labeled as one class (majority class), and the rest as the other, commonly considered as the class of interest (minority class). The proposed methodology does not depend on the applied classification models, rather it is based on the properties of the data resampling approach to systematically enhance and optimize the training dataset. The paper examines numerical experiments applying the data centric optimization methodology, and compares with previously obtained results by other authors. [ABSTRACT FROM AUTHOR]

• A novel global optimization algorithm, rime optimization algorithm (RIME), is proposed. • RIME simulates the growth and crossover behavior of the rime-particle population. • The performance of RIME is adequately discussed based on benchmark functions. • RIME has better convergence accuracy and speed compared to other methods. • RIME is applied to solving five practical engineering optimization problems. This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice, called the RIME. The RIME algorithm implements the exploration and exploitation behaviors in the optimization methods by simulating the soft-rime and hard-rime growth process of rime-ice and constructing a soft-rime search strategy and a hard-rime puncture mechanism. Meanwhile, the greedy selection mechanism in the algorithm is improved, and the population is updated in the stage of selecting the optimal solution to enhance the exploitation capability of the RIME. In the experimental, this paper conducts qualitative analysis experiments on the RIME to clarify the characteristics of the algorithm in the process of finding the optimal solution. The performance of RIME is then tested on a total of 42 functions in the classic IEEE CEC2017 and the latest IEEE CEC2022 test sets. The proposed algorithm is compared with 10 well-established algorithms and 10 latest improved algorithms to verify its performance advantage. In addition, this paper designs experiments for the parametric analysis of RIME to discuss the potential of the algorithm in running different parameters and handling different problems. Finally, this paper applies RIME to five practical engineering problems to verify its effectiveness and superiority in real-world problems. The statistical and comparison results show that the RIME is a strong and competitive algorithm. The source code of the RIME 1 1 Source codes of RIME algorithm are also available at: https://codeocean.com/capsule/4472937/tree and https://www.mathworks.com/matlabcentral/fileexchange/124610-rime-a-physics-based-optimization and https://github.com/aliasgharheidaricom/RIME-A-physics-based-optimization. algorithm and associated files are publicly accessible at https://aliasgharheidari.com/RIME.html. [ABSTRACT FROM AUTHOR]

Various strategies for optimizing the renewable energy system size exist, and they vary depending on the system design. In this paper, a standalone hybrid renewable energy system composed of a photovoltaic power source, wind turbines, storage, and diesel generators was considered. Linear programming was used to optimize the system in terms of multiple objectives. Bender's decomposition, lexicographic optimization, and epsilon constraint are among the linear programming techniques employed. The main objective function is to keep excess power to a minimum while also keeping unsatisfied demand to a minimum. This paper considers a full-year time span, optimizes household self-consumption, reduces curtailed power, maximizes local use of renewable generated power, lowers operational costs, lowers CO2 emissions, considers multiple renewable energy sources, considers system sizing as an objective, and incorporates the net present worth value of the components into the sizing. The aforementioned objectives and considerations have never been brought together yet in the literature. Furthermore, using a 365-day span is more comprehensive than using a 24-h span, which is typically used to limit the size of the linear programming issue. Therefore, in terms of integrated objectives, cost analysis, considerations, and timeframe, the proposed method is considered one of the most comprehensive. The ability to adjust the feed in limit to the grid and the change in electricity price during the day are also considered in this work. [ABSTRACT FROM AUTHOR]

In order to mitigate the impact of temperature and irradiation variations on photovoltaic (PV) panels, it is crucial to employ the maximum power point tracking (MPPT) technique. This paper introduces a novel MPPT approach that provides a reference current, denoted as Iref, to extract the maximum power from the PV array. The difference between the input current Ipv, and Iref is then fed into a PID controller, which adjusts the duty cycle accordingly to control the Quadratic Boost Converter (QBC). The proposed MPPT approach is based on the analysis of measurement data obtained from PV panels. From this, a linear mathematical function between Iref and solar irradiation is achieved. To assess the performance of this approach, a comparative study is being carried out not only with conventional MPPT techniques such as P&O and INC-COND but also with modern techniques such as ANN, PSO, FLC, and improved P&O (IMP-P&O). The simulation results clearly demonstrate enhanced tracking performance under various test conditions, including the Step & Ropp test, as well as real profiles for both sunny and cloudy days. Certainly, the novel approach shows its ability to outperform both conventional and modern methods with a faster convergence speed (less than 2 milliseconds), the lowest steady-state oscillations (less than 0.25 Watts), and the highest tracking efficiency (greater than 99.79%). [ABSTRACT FROM AUTHOR]

Severe droughts, water-use conflicts, and minimum outflow requirements have caused infeasibilities in modeling large-scale complex hydrothermal systems. This paper presents and discusses a new formulation of objective function and constraints to better represent multiple water uses and deal with infeasibilities in the HIDROTERM model, a nonlinear programming (NLP) model developed to optimize the operation of the Brazilian interconnected hydrothermal system. The system is one of the largest in the world. In the last 10 years, approximately 72% of electricity consumed in Brazil has been supplied by hydropower plants, but with increasing shares from thermal, wind, and, more recently, solar power generation. Because of hydrological changes in recent years and the additional operational constraints imposed by environmental agencies, the use of models for decision making, such as HIDROTERM, frequently encounters the problem of infeasibility. To avoid this problem, a new objective function is proposed in HIDROTERM that incorporates economic penalties when the preset requirements are not met. Based on economic considerations, some hard constraints are removed from the constraint set and added to the objective function as penalty terms. These constraints are tightly linked with multiple water uses, including environmental protection, flood control, consumptive uses, and navigation purposes. The proposed change in the objective function and constraints avoids the problem of infeasibilities and makes it possible to apply the model to extreme conditions. Additionally, this formulation helps identify and minimize the risk, duration, and intensity of unavoidable non-compliance occurrences related to multiple water uses. The presented new version of the HIDROTERM model can be used to study and support decision making in the management and operation of large-scale hydrothermal systems. These systems can be formed by a set of hydraulically connected individual hydropower plants and reservoirs, located within a set of electrically connected subsystems. Each subsystem contains demands and other power plants, including thermal, wind, nuclear, and solar plants, and exchanges. The model has been developed with continued improvements since 2008 and applied to large systems such as the Brazilian Interconnected Power System (BIPS) with over 150 reservoirs. A graphical user interface is provided. The objective function is to minimize the total costs of additional thermal dispatch and exchanges, as well as avoid or minimize deficits in energy supply in subsystems, minimum releases, consumptive uses, and minimum storages in reservoirs. The decision variables are the power and nonpower releases for each hydropower plant and the thermal dispatches and exchanges for each subsystem. Some typical applications include (1) monthly planning operation of the system, considering a planning horizon from 3 to 5 years; (2) diagnostic studies about reliability and resilience of existing systems, processing the same system configuration to a set of different hydrological or energy demand scenarios; (3) impact analysis of changes in operational rules and constraints related to multiple uses of water; and (4) planning and evaluation of expansion alternatives. The model can be applied to a complete hydrothermal system or subsets of it, including subsystems, basins, cascades, or a single hydropower plant. [ABSTRACT FROM AUTHOR]

In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences. Consequently, this paper proposes an enhanced evolutionary-based approach that simplifies the sequence alignment problem without considering the sequences in the non-dominated solution. Our method employs a multi-objective optimization technique that uniquely excludes non-dominated solution sets, effectively mitigating computational complexities. Utilizing the Sum of Pairs and the Total Conserved Column as primary objective functions, our approach offers a novel perspective. We adopt an integer coding approach to enhance the computational efficiency, representing chromosomes with sets of integers during the alignment process. Using the SABmark and BAliBASE datasets, extensive experimentation is conducted to compare our method with existing ones. The results affirm the superior solution quality achieved by our approach compared to its predecessors. Furthermore, via the Wilcoxon signed-rank test, a statistical analysis underscores the statistical significance of our model's improvement (p < 0.05). This comprehensive approach holds promise for advancing Multiple Sequence Alignment in bioinformatics. [ABSTRACT FROM AUTHOR]

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids. [ABSTRACT FROM AUTHOR]

Despite the significance of resource levelling, project managers lack various ways to smooth resource usage fluctuation of a repetitive construction project besides changing resource usage. Tolerating interruptions is an effective way to provide flexibility for a schedule but is ignored when solving resource levelling problems. Therefore, this paper investigates the impacts of interruptions on resource usage fluctuation and develops an integrated approach that simultaneously integrates two scheduling adjusting processes: changing resource usage and tolerating interruptions. In this paper, two interruption conditions are proposed to identify which activities are suitable to be interrupted for smoothing resource usage fluctuation. The traditional resource levelling model is modified to a new scheduling model by incorporating interruptions. A two-stage GA-based scheduling algorithm is developed by integrating changing resource usage and tolerating interruptions. A commonly used pipeline project is adopted to illustrate the steps of the proposed approach and demonstrate its effectiveness and superiority through comparison with previous studies. A large-scale project further verifies the usability of the proposed approach. The results confirmed the feasibility to smooth resource usage fluctuation by interruptions, and the integrated approach can achieve a more competitive resource levelling result. [ABSTRACT FROM AUTHOR]

The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques. [ABSTRACT FROM AUTHOR]

We have designed and implemented an optimized PID controller for an adaptive cruise control system in this paper. The mathematical model for a cruise control system has been developed, and it is observed that it is a nonlinear first-order model with dead time. The objective functions chosen for optimizing the PID controller are ITE, ITAE and ITSE. The design of the optimized PID controller is based on the particle swarm optimization technique and teacher learning-based optimization technique. The results are scientifically compared with the conventionally tuned PID and fuzzy-based controllers. The optimized Proportional Integral Derivative controller shows better performance than a conventional PID and fuzzy-based controller. The overshoot of the system has been reduced to 0% from 46%, and the rise time has been reduced to 0.6150 s. This is the new work in the literature that will be quite useful for the performance enhancement of the cruise control system. [ABSTRACT FROM AUTHOR]

Phase field modeling finds utility in various areas. In optimization theory in particular, the distributed control and Neumann boundary control of phase field models have been investigated thoroughly. Dirichlet boundary control in parabolic equations is commonly addressed using the very weak formulation or an approximation by Robin boundary conditions. In this paper, the Dirichlet boundary control for a phase field model with a non-singular potential is investigated using the Dirichlet lift technique. The corresponding weak formulation is analyzed. Energy estimates and problem-specific embedding results are provided, leading to the existence and uniqueness of the solution for the state equation. These results together show that the control to state mapping is well defined and bounded. Based on the preceding findings, the optimization problem is shown to have a solution. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to suppressing bounded exogenous disturbances in a linear discrete-time control system by a static state- or output-feedback control law. The approach is based on reducing the original problem to a nonconvex matrix optimization problem with the gain matrix as one variable. The latter problem is solved by the gradient method; its convergence is theoretically justified for several important special cases. An example is provided to demonstrate the effectiveness of the iterative procedure proposed. [ABSTRACT FROM AUTHOR]

The nozzle-matrix gas flow velocity has a great influence on the accuracy of the temperature field of a printed circuit board assembly (PCBA) during the hot air convection reflow soldering process. This paper proposes a new approach that integrates the theoretical calculation, numerical simulation and an experimental test to accurately determine the nozzle-matrix gas flow velocity. First, the temperature profile of the aluminum alloy thin plate in convection reflow ovens is measured using a Wiken tester. Second, the nozzle-matrix gas flow velocity is theoretically calculated with the Martin formula. The computational fluid dynamic (CFD)simulation is performed according to the Icepak code, where a single oven chamber model is established to represent the 10 zones of soldering ovens to reduce computational resources considering the supry of the soldering ovens. The simulated temperature profile of the aluminum alloy thin plate is obtained and the specific response surface model (RSM) is established to represent the deviation between the simulated temperature and the measured temperature. Finally, based on reverse problem analysis, non-linear programming by quadratic Lagrangian (NLPQL) is used to solve the mathematical optimization model with the objective of minimizing the temperature deviation to obtain the corrected nozzle-matrix gas flow velocity. To validate the accuracy, the temperature test and the modeling using the corrected gas flow velocity for a new PCBA component for the soldering ovens is conducted separately. The temperature comparison between the simulation and the test shows that the maximum temperature deviation is within 10 °C. This provides evidence that the nozzle-matrix gas flow velocity obtained by the new approach is accurate and effective. [ABSTRACT FROM AUTHOR]

This paper explores the concept of Generalized Roughness in LA-Semigroups and its applications in various mathematical disciplines. We highlight the fundamental properties and structures of Generalized Roughness, examining its relationships with Fuzzy Lie Algebras, Order Theory, Lattice Structures, Algebraic Structures, and Categorical Perspectives. Moreover, we investigate the potential of mathematical modeling, optimization techniques, data analysis, and machine learning in the context of Generalized Roughness. Our findings reveal important results in Generalized Roughness, such as the preservation of roughness under the fuzzy equivalence relation and the composition of roughness sets. We demonstrate the significance of Generalized Roughness in the context of order theory and lattice structures, presenting key propositions and a theorem that elucidate its properties and relationships. Furthermore, we explore the applications of Generalized Roughness in mathematical modeling and optimization, highlighting the optimization of roughness measures, parameter estimation, and decision-making processes related to LA-Semigroup operations. We showcase how mathematical techniques can enhance understanding and utilization of LA-Semigroups in practical scenarios. Lastly, we delve into the role of data analysis and machine learning in uncovering patterns, relationships, and predictive models in Generalized Roughness. By leveraging these techniques, we provide examples and insights into how data analysis and machine learning can contribute to enhancing our understanding of LA-Semigroup behavior and supporting decision-making processes. [ABSTRACT FROM AUTHOR]

The regulation and stabilization of a system's output to achieve desired performance and ensure system reliability require the use of different controllers, but selecting appropriate control parameters presents a challenge in ensuring robustness and stability. Proportional–integral–derivative (PID) controllers are popularly used due to their simplicity and effectiveness in addressing these challenges. In this paper, a simple, effective, and efficient, novel graphical technique is proposed to design PID and its variants (PI/PD) controllers, which addresses the challenges in selecting appropriate control parameters. The method involves creating three equispaced vectors for controller parameters (Kp,Ki,Kd)$(K_{\text{p}}, K_{\text{i}}, K_{\text{d}})$, and obtaining a 3D (2D in PI/PD) Cartesian grid of these vectors. All nodes in the grid provide several possible controllers, and integral time squared error (ITSE) is calculated for each controller from the closed‐loop step response of the system. The obtained ITSE is plotted in a 4D (PID) or 3D (PI/PD) graph, and controller parameters corresponding to the minimum value of ITSE are identified. Furthermore, the proposed graphical technique aids in choosing the lower and upper bounds (LB and UB) if the controller is designed using optimization techniques. The better selection of LB and UB reduces the search space resulting in lesser execution time and fewer iterations. To validate the proposed graphical technique, we designed various controllers for widely‐used brush‐less DC and switch reluctance motors in electric vehicles. Additionally, by choosing the LB and UB with the proposed technique, controllers are also designed using three optimization techniques: particle swarm optimization, black widow optimization algorithm, and honey badger algorithm. The obtained controllers using the graphical technique outperformed the optimization techniques in terms of time and frequency domain specifications, and the proposed selection of lower and upper bounds resulted in improved performance in terms of iterations and execution time. [ABSTRACT FROM AUTHOR]

This paper describes the virtual design of a 22nm gate length p-type metal oxide semiconductor, PMOS. Silvaco, TCAD tools were used to fabricate the device design and to characterize the device's electrical properties. Fixed field scaling rules are applied to obtain the transistor's electrical parameters set by ITRS 2013. In order to take the challenges that arise in the fabrication of nano-sized transistors and enhance their performance, advanced and novel technologies are applied. Using the statistical modelling of L9 Taguchi methodology, the development process is primarily focused on the tool's edge voltage. Four parameters have been divided into three distinct steps in order to conduct nine different experiments. The final confirmation result indicates that VTH is closer to the nominal value -0.206V following optimization techniques. This matches the ITRS 2013 requirements for high performance. This paper examines the design of a p-MOS double gate containing a layer of graphene as it is known to have a high mobility value. [ABSTRACT FROM AUTHOR]

The most commonly used model of solar cells is the single-diode model, with five unknown parameters. First, this paper proposes three variants of the single-diode model, which imply the voltage dependence of the series resistance, parallel resistance, and both resistors. Second, analytical relationships between the current and the voltage expressed were derived using the Lambert W function for each proposed model. Third, the paper presents a hybrid algorithm, Chaotic Snake Optimization (Chaotic SO), combining chaotic sequences with the snake optimization algorithm. The application of the proposed models and algorithm was justified on two well-known solar photovoltaic (PV) cells—RTC France solar cell and Photowatt-PWP201 module. The results showed that the root-mean-square-error (RMSE) values calculated by applying the proposed equivalent circuit with voltage dependence of both resistors are reduced by 20% for the RTC France solar cell and 40% for the Photowatt-PWP201 module compared to the standard single-diode equivalent circuit. Finally, an experimental investigation was conducted into the applicability of the proposed models to a solar laboratory module, and the results obtained proved the relevance and effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an 'Internet of vehicles' with the potential to significantly enhance citizens' mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems. [ABSTRACT FROM AUTHOR]

This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed. [ABSTRACT FROM AUTHOR]

This article considers a box-constrained global optimization problem for Lipschitz continuous functions with an unknown Lipschitz constant. The well-known derivative-free global search algorithm DIRECT (DIvide RECTangle) is a promising approach for such problems. Several studies have shown that recent two-step (global and local) Pareto selection-based algorithms are very efficient among all DIRECT-type approaches. However, despite its encouraging performance, it was also observed that the candidate selection procedure has two possible shortcomings. First, there is no limit on how small the size of selected candidates can be. Secondly, a balancing strategy between global and local candidate selection is missing. Therefore, it may waste function evaluations by over-exploring the current local minimum and delaying finding the global one. This paper reviews and employs different strategies in a two-step Pareto selection framework (1-DTC-GL) to overcome these limitations. A detailed experimental study has revealed that existing strategies do not always improve and sometimes even worsen results. Since 1-DTC-GL is a DIRECT-type algorithm, the results of this paper provide general guidance for all DIRECT-type algorithms on how to deal with excessive local refinement more efficiently. [ABSTRACT FROM AUTHOR]

The possibilities and ways of design and implementation of distributed information and communication systems for management of traffic and road infrastructure in road traffic that are used in intelligent transportation systems, are considered and described in the paper. First, such intelligent transportation systems are shortly described. Then, the basic architecture of one distributed information and communication system for intelligent management in road traffic is proposed. The system consists of a central part of the system and several modules or subsystems, responsible for certain individual functionsof control. Most of the paper is dedicated to the description of one practically designed and implemented module, or system, for optimizing travel and travel costs. The design and architecture of the implemented system are described. The system is intended for optimization of the length, time and cost of traveling of users in road traffic. It offers to the user several of the most favourable routes, i.e. traveling routes, between selected travel destinations, with their lengths, expected travel times and expected travel costs, on journeys that cross state borders and use roads with toll payment. It takes into account the duration of congestion at border crossings and toll payment stations, as well as the prices of tolls on the offered traveling routes. More detailed experimental research was performed on the designed and implemented system. Situations when crossing the border between Bosnia and Herzegovina and Serbia, the border between Bosnia and Herzegovina and Croatia, and the border between Serbia and Croatia were observed practically and experimentally. Also, toll payment stations on highways in Republic of Srpska, Serbia and Croatia were observed. Some experimentally obtained results are given in the paper. [ABSTRACT FROM AUTHOR]

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21% in the global planning stage and the motion efficiency of the robot is improved by 22.87% compared with the recent quintic Bézier curve-based state space sampling approach. We name the proposed motion planning framework E $\mathbf {^{3}} $ MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages. Note to Practitioners—This paper is motivated by the challenges of motion planning problems of mobile robots. A three-layer motion planning framework is proposed by combining global path planning, local path optimization, and time-optimal velocity planning. For mobile robot navigation applications in semi-structured environments, optimization-based local planners are recommended. Extensive simulation and experimental results show the effectiveness of the proposed motion planning framework. However, due to the non-convexity of the path optimization formulation, the proposed local planner may get stuck in local optima. In future research, we will concentrate on extending the proposed local path optimization approach with the theory of homology classes to maintain several homotopically distinct local paths and seek global optima. [ABSTRACT FROM AUTHOR]

The paper presents research on a specific approach to the issue of computed tomography with an incomplete data set. The case of incomplete information is quite common, for example when examining objects of large size or difficult to access. Algorithms devoted to this type of problems can be used to detect anomalies in coal seams that pose a threat to the life of miners. The most dangerous example of such an anomaly may be a compressed gas tank, which expands rapidly during exploitation, at the same time ejecting rock fragments, which are a real threat to the working crew. The approach presented in the paper is an improvement of the previous idea, in which the detected objects were represented by sequences of points. These points represent rectangles, which were characterized by sequences of their parameters. This time, instead of sequences in the representation, there are sets of objects, which allow for the elimination of duplicates. As a result, the reconstruction is faster. The algorithm presented in the paper solves the inverse problem of finding the minimum of the objective function. Heuristic algorithms are suitable for solving this type of tasks. The following heuristic algorithms are described, tested and compared: Aquila Optimizer (AQ), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Dynamic Butterfly Optimization Algorithm (DBOA). The research showed that the best algorithm for this type of problem turned out to be DBOA. [ABSTRACT FROM AUTHOR]

Electric distribution grid operations typically rely on both centralized optimization and local non-optimal control techniques. As an alternative, distribution system operational practices can consider distributed optimization techniques that leverage communications among various neighboring agents to achieve optimal operation. With the rapidly increasing integration of distributed energy resources (DERs), distributed optimization algorithms are growing in importance due to their potential advantages in scalability, flexibility, privacy, and robustness relative to centralized optimization. Implementation of distributed optimization offers multiple challenges and also opportunities. This paper provides a comprehensive review of the recent advancements in distributed optimization for electric distribution systems and classifications using key attributes. Problem formulations and distributed optimization algorithms are provided for example use cases, including volt/var control, market clearing process, loss minimization, and conservation voltage reduction. Finally, this paper also presents future research needs for the applicability of distributed optimization algorithms in the distribution system. [ABSTRACT FROM AUTHOR]

Compilation often takes a long time, especially for large projects or when identifying better optimization options. Currently, compilers are mainly installed on local machines and used as standalone software. Despite the availability of several online compilers, they do not offer an efficient all-in-one package for private account management, command line interface (CLI), code advisors, and optimization techniques. Today, the widespread usage of Software as a Service (SaaS) is ever-growing, and compilers are not an exception. In this paper, we describe a symmetric approach to compilation and how to compile code on distributed systems. Although some improvements in cloud compilers have been made, it is possible to harness the potential of the most-modern technologies and architecture patterns toward designing efficient, in-cloud compilers. In this paper, we propose an architecture design of a cloud-based compiler that is fully compatible with orchestration technologies, such as Kubernetes, providing a higher level of scalability, reliability, security, and maintainability. Microservice architecture alongside containerization and orchestration technologies assist us in making a scalable system that provides a high level of availability. We propose this architecture so that the system can handle a higher workload as it receives a large number of compilation requests per second. Distributed compilation is a prominent benefit of this approach, as each phase of the compilation can be executed in a separate server, which supplies a kind of workload mitigation to the whole system. In other words, we propose a new perspective for an intelligent way of advisor, error detection, and optimization of compilers. We also propose an implementation example of the developed architecture. Finally, we analyze the results from an experimental implementation, proving that we can compile code from more than 100k requests concurrently on a cloud cluster with one master node and three worker nodes. [ABSTRACT FROM AUTHOR]

In general, a complex system is composed of different components that are usually subject to a maintenance policy. We take into account systems containing components that are under both preventive and corrective maintenance. Preventive maintenance is considered as a failure-based preventive maintenance model, where full renewal is realized after the occurrence of every nth failure. It offers an imperfect corrective maintenance model, where each repair deteriorates the component or system lifetime, the probability distribution of which gradually changes via increasing failure rates. The reliability mathematics for unavailability quantification is demonstrated in the paper. The renewal process model, involving failure-based preventive maintenance, arises from the new corresponding renewal cycle, which is designated a real ageing process. Imperfect corrective maintenance results in an unwanted rise in the unavailability function, which can be rectified by a properly selected failure-based preventive maintenance policy; i.e., replacement of a properly selected component respecting both cost and unavailability after the occurrence of the nth failure. The number n is considered a decision variable, whereas cost is an objective function in the optimization process. The paper describes a new method for finding an optimal failure-based preventive maintenance policy for a system respecting a given reliability constraint. The decision variable n is optimally selected for each component from a set of possible realistic maintenance modes. We focus on the discrete maintenance model, where each component is realized in one or several maintenance mode(s). The fixed value of the decision variable determines a single maintenance mode, as well as the cost of the mode. The optimization process for a system is demanding in terms of computing time because, if the system contains k components, all having three maintenance modes, we need to evaluate 3k maintenance configurations. The discrete maintenance optimization is shown with two systems adopted from the literature. [ABSTRACT FROM AUTHOR]

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [ABSTRACT FROM AUTHOR]

Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions. [ABSTRACT FROM AUTHOR]

Waterflooding is the most common secondary recovery technique used in oil and gas industries today, owing to its cheap investment cost and easy implementation. However, major challenges are encountered in terms of oil sweep efficiency and breakthrough time which poses a risk to production and economic lifecycle of reservoirs. As a result, reservoir engineers are tasked with improvising optimal production strategies with the goal of maximizing profit. This review extensively describes some common optimization techniques reported in improving oil reservoir production. Also, their formulation, limitations and advantages with respect to production rates, oil well placement and control, inter-well connectivity and reservoir sweep efficiency were reviewed. While there are several optimization algorithms used in waterflooding, the emphasis in this work involves only the gradient and data driven optimizers since it is impossible to cover all optimization technique in a single review paper. Basically, no algorithm has been globally accepted as superior to the other since the sole aim is to improve productivity and economic profit, and each of these techniques has its unique practicability. However, when considering factors like design limitations, computational and economic cost, implementation timeframe, availability of data, some technique may suffice. [ABSTRACT FROM AUTHOR]

This paper presents a feasibility analysis of the technical, environmental, and economic sustainability of an existing mini-grid technology system in Nigeria. The study investigates the cost and other operational parameters of the Gbamu-Gbamu solar–battery–diesel hybrid mini-grid, specifically the 85 kWp solar PV installation in the Ijebu East Local Government area of Ogun state. Situated within the Owo forest in South-West Nigeria, the mini-grid aims to reduce the effects of global warming and promote sustainable technological development in rural communities by increasing energy access through renewable sources. To assess the system's viability, this research utilized RETScreen Expert software to validate the techno-economic and environmental sustainability of the installed mini-grid solar–PV–battery–diesel system in the region. Climatic data for the study were obtained from the National Aeronautics and Space Administration (NASA). The results demonstrate that the system is economically feasible and environmentally viable, as indicated by the positive net present value (NPV) and an average monthly irradiance of 4.78 kW/h/m2. Furthermore, the system achieved a 92.9% reduction in GHG emissions, provided a reasonable payback period of four years, and enabled a yearly electricity export of 203 MWh. These findings highlight the system's potential to enhance energy access and mitigate climate change. [ABSTRACT FROM AUTHOR]