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The study here summarizes the work growth in "Particle Swarm Optimization (PSO)" since (1995 to 2021). Thirty-five papers have been analysed. Those research articles have been divided into various aspects in which PSO technique has been modified a lot during these years due to its simplicity. The hybridization of PSO with many evolutionary algorithms has been done during these years which helped us a lot to solve many optimizations and real-life problems. This paper discusses the progress, modification, improvement and application of PSO during these years. [ABSTRACT FROM AUTHOR]

Heart disease is a major killer in the world. Nature-inspired algorithms have been used as optimization techniques to predict the disease. The prediction of the disease can be further enhanced by using classification models of machine learning algorithms. The accuracy and other aspects of prediction varies with classification models. This paper presents the research conducted by combining nature-inspired techniques with machine learning algorithms to make intelligent decisions for prediction of heart diseases. As part of the research conducted, the prediction of the heart disease has been assessed by the combination of nature-inspired techniques and machine learning algorithms. This paper presents the outcome of the research conducted with four nature-inspired algorithms, namely Ant, Bat, Bee and Genetic algorithms and two machine learning classification models, Random Forest and SVM to predict the heart disease. Feature variables are extracted from heart data obtained from the UCI Machine Learning Repository. The output of the optimization techniques is used to train models based on the two classification algorithms. The measures of prediction were compared. [ABSTRACT FROM AUTHOR]

Investment basically means to indulge the money into an investment alternative for the purpose of profit and income stability, such as gold, fixed deposits, real estate and cryptocurrency, people who invest known are as investors. Maintaining risk in individual positions and throughout the portfolio as a whole is critical to being a successful investor and trader. However, with so many different approaches to risk management at these two levels, it can be tough to figure out which one is ideal for your own style and risk tolerance. For making it easier and efficient there are some techniques and strategies which are Market Cap Weighted Portfolio, Sharpe Ratio, Global Efficient Frontier AKA MSR, Mean-Variance (Markowitz), Minimum Variance, Hierarchical Risk Parity (HRP), Cvar and Var, equal weighted portfolio and minimum variance. This research paper is concrete scope review of all these techniques and strategies. [ABSTRACT FROM AUTHOR]

Poor power quality is a major concern in power system, which can lead to equipment damage, power interruptions, and safety hazards. In modern days, due to non-linear loads like UPS, inverters, converters, etc., the supply current takes on a non-sinusoidal shape. Multilevel inverters are widely used in power systems to improve power quality in higher voltage levels with fewer switches and by reducing harmonic distortion which improves the overall efficiency of the system. Several optimization techniques can be used to design and control multilevel inverters for improved power quality performance. The important object of this review paper is to identify various optimization techniques while taking various power quality-related challenges into account. Thus, the current study analyses different power quality issues and their improvement with multilevel inverters. The paper also discusses overviews of various technical and common issues presented in the literature, all carried out in a comprehensive manner. Finally, this paper offers a scenario of the current state of power quality improvement along with different optimization techniques which discovers research gaps along with suggestions for future research directions. [ABSTRACT FROM AUTHOR]

This paper presents the comparison ofselective harmonic elimination in a cascaded h-bridge multilevel (CHBMLI) inverter using PSO and TLBO optimization algorithms. The inverters are employed in a variety of applications, including high-voltage dc transmission, variable drive systems, and electrical vehicles. The inverter's output includes harmonics. PSO and TLBO algorithm is used in this paper To find out the best switching angles for a CHBMLI selective harmonic elimination. In order to get the appropriate fundamental component and get rid of low-order harmonics, this project solves nonlinear equations. These algorithms can produce the most effective triggering pulses to minimize Overall Harmonic Distortion(THD) and Selected Harmonics. If the harmonics are removed, the inverter's output will be improved. MATLAB is used to implement and compared both optimization techniques usingthe seven-level inverter. [ABSTRACT FROM AUTHOR]

This paper presents three types of optimisation techniques namely particle swarm optimisation, artificial bee colony optimisation, and grey wolf optimisation. These optimisation approaches are employed to determine the optimal voltage collapse point (VCP) of the power system weakest bus without having to perform several power flow calculations. Moreover, this study is employed in the simplified Department of Electrical Services (DES) network in Brunei Darussalam. The optimality of the results is aided with reactive power-voltage (QV) modal analysis. Furthermore, the results obtained from the employed methods were compared with the results obtained from power system simulator for engineering (PSSE) 34 software. In the end, the results of each analysis were discussed and compared to show its effectiveness in determining the VCP of the weakest bus. [ABSTRACT FROM AUTHOR]

Nature inspired techniques are most popular techniques used for optimization. Clustering is a data mining functionality which is also used in optimization. In recent past, researchers have implemented several nature inspired techniques using clustering. In this paper, scientometric analysis is conducted to analyze the research trends of these techniques using data from Web of Science and Scopus databases where analysis of ACO, PSO, Bat, Lion, water droplets, and such optimization techniques inspired by nature are studies which use k-means, DB Scan, k-neighbor and other such clustering algorithms. Data is analyzed globally, cluster analysis of related keywords in analyzed along with link strength. Various other experiments are also conducted which help analyze research trends in clustering and nature inspired techniques. [ABSTRACT FROM AUTHOR]

This dissertation concentrates on the application of non-traditional optimization techniques. To solve the optimization problem, the various non-traditional optimization methods are reviewed critically. The methods that are going to review in this survey are Particle Swarm optimization (PSO), Crow Search Algorithm (CSA), Charged system search algorithm (CSSA) and Big-bang big crunch optimization (B-BBCO). These methods are applied to investigate the optimum values. The both linear and non-linear optimization problems are solved to get global solutions. [ABSTRACT FROM AUTHOR]

A shaft is used to transmit power from a device that produces power to a gadget that absorbs energy. The strength of the hollow shaft is greater than the equal weight stable shaft. Hollow shafts have a greater polar second of inertia, which permits them to transmit greater torque than stable shafts. Non-Traditional Optimization strategies are efficaciously used to resolve many engineering problems. This paper uses ten non-traditional optimization techniques to discover the weight minimization of a hollow shaft. The comparative outcomes display that the Particle Swarm Optimization outperforms different techniques. [ABSTRACT FROM AUTHOR]

The paper proposes a new approach to solving the optimization problem for a system of processing technogenic waste of the oil and gas industry. The optimization problem is formulated as an integer mathematical programming problem for the maximum of the objective function considering the constraints. The objective function in the optimization problem is determined using a set of efficiency assessments of environmental safety, resource, and energy consumption in the waste processing system. These assessments are obtained via multi-factorial analysis based on the Data Envelopment Analysis (DEA) method. As the constraints to the problem solution, the limitations on the maximum operating time of the technological plant for the recycling of the waste placed in the storage facility are considered. Special software is developed for the numerical solution of the optimization problem. [ABSTRACT FROM AUTHOR]

Cement-based composites have a very broad field of applications but their composition has to be designed with care to meet the desired functional properties. High-temperature resistant mixtures can be stated as a characteristic example in that respect. The standard calcium silicate binder should be replaced usually by high alumina cements which can be supplemented by waste materials or byproducts within the global environmental protection efforts. To reach the optimal properties and composition, advanced optimization techniques are supposed to be used rather than empirical approaches. This paper presents an application of downhill simplex method to optimize the composition of a binder based on calcium alumina cement and supplemented by ceramic powder, super reactive alumina and boehmite powder. The optimization procedure results in a 7% improvement of compressive strength, which reaches ∼117 MPa. Due to the high temperature resistant nature of all substances used (chamotte and corundum as fillers), the proposed mixture is supposed to resist increased temperatures up to 1400 °C. [ABSTRACT FROM AUTHOR]

In the design of positive displacement compressors, the mathematical relationship between design parameters and design objectives is usually impractical and costly to be extracted, making the optimization process becomes a 'black- box' problem. In the available literature, the Bayesian optimization method, compared to other optimization techniques, has been proven as an accurate and efficient method to solve the 'black-box' problem. However, the application of such a method in the design of the rotary compressor has not been discussed in published papers. Hence, the current study is intended to employ Bayesian optimization to geometric design a class of positive displacement compressors, which is known as the limaçon compressor. In this paper, the geometric characteristics of the limaçon compressor are presented, and a function, which incorporates volumetric and geometric aspects, is employed to evaluate the optimization process and to reveal the optimum design scenario as per design requirements. A case study is offered to prove the validity of the presented approach. [ABSTRACT FROM AUTHOR]

The inflation phenomenon is a common factor for today's inventory system as it is increasing with time in the real world. Every industrial corporation is performing the optimization of their current inventory techniques to achieve the highest profit at minimum cost. In this scenario, the major problem, they are facing, is the problem of deterioration which contributes to the increment of the total cost. To reduce this loss of deteriorating items; organizations are investing in preservation technology. In this present paper, an economic order quantity model is considered, in which it is assumed that the demand rate is depending on stock and selling price factors under the effect of inflation over time. The variable holding cost is assumed as a linear function of time. The main intention of this initiated paper is to find the optimal investment in preservation technology in the context of inflation so that total cost will be minimized. The case of without preservation technology and many more cases are also discussed. A numerical example with these special cases has been also discussed. Finally, the sensitivity analysis is also studied to provide several observations. [ABSTRACT FROM AUTHOR]

Water is essential for all human activities, and that rainfall is one of the critical sources of this precious commodity. Prediction of how much rainfall and when it is most likely to occur will assist concerned officials in planning for its storage and subsequent distribution. Meteorological agencies predict rainfall using statistical or dynamic models. Because of the complexity involved in rainfall prediction and limitations of existing techniques, prediction skill improvement is necessary. Recently, researchers in prediction are using intelligent systems such as Artificial Neural Networks, Fuzzy Inference Systems, Support Vector Machines, and Genetic Algorithms. Many network parameters are required to be selected for the use of these systems, and the choice of the parameters affects the accuracy of the model. Experimental discovery of the parameters is one way, and the other way is to use optimization algorithms. In this paper, various optimization techniques used in computationally intelligent systems are surveyed for rainfall prediction. The optimization techniques mainly used for this purpose are Particle Swarm Optimization, Genetic Algorithm, and Ant Colony Optimization. In all the research articles case study of a certain geographical area for rainfall prediction with a different set of inputs and different forecasting lead times is presented, and hence comparison between the models is difficult. For rainfall prediction, model input selection is equally important to the selection of model parameters as a set of predictors change with increasing or decreasing geographical area and forecast lead time. This paper attempts to identify optimization techniques suitable for medium-range rainfall forecasting over a small geographical area. [ABSTRACT FROM AUTHOR]

The application of complex nonlinear material models of concrete within the numerical simulations is currently the most used way for the purposes of modeling the nonlinear computer response of concrete structures. However, the modeling of nonlinear concrete behavior through the nonlinear material models very often brings with it the major problem. This problem can be specified as a user nescience of a large number of material model parameters which is very common. Nowadays, the solution of this problem is possible with the so called inverse analysis or identification which can be performed via the different methods and approaches. One of the most effective approach for the purposes of the inverse analysis is currently using the combination of sensitivity analysis with optimization algorithms. This mentioned approach is demonstrated within this paper in which the parameters of the material model called the Continuous Surface Cap Model are identified. Material parameter identification is performed on the basis of interaction between nonlinear numerical simulations, statistical and optimization methods and experimental data which are represented by a load-extension curve obtained from the unconfined uniaxial tensile (UUT) test. The final comparison of the experimental data with the numerically-simulated data for optimal parameter values is, of course, also part of this paper. [ABSTRACT FROM AUTHOR]

Generation of very fast transient over voltages (VFTOs) is very common during switching operations in gas insulated substations (GIS) and it is extremely harmful for substation equipment like transformers as it becomes a reason of insulation damage due to its less arising time. Sometimes, the VFTOs magnitude may be very near to the basic insulation level (BIL). Improvement and investigation of such transients in power transformer is of crucial importance in GIS as transformers are vital components of power system network. Generation of transient issues considers the generation and propagation of VFTOs while switching of circuit breakers or disconnector switches or earth faults. In this research, the investigation is on VFTO by putting intelligible models for a GIS of a capacity 400 KV. This paper presents a modeling guideline for VFTO analysis in GIS and, further mitigation techniques such as Nano-crystalline Rings, RC Filter, Ferrite Rings, Damping Resistance etc. have been studied using MATLAB simulation. Using an intelligent optimization technique like PSO, VFTO waveforms can be scaled down further to controllable limits so that substation components can be saved from impending failures. Further VFTO effect on GIS such as stress on substation Transformer leading to winding deformation has been discussed in this paper and localization of deformation and type of deformation in transformer winding is also carried out using intelligent classifier. [ABSTRACT FROM AUTHOR]

Optimization of well placement plays a considerable role in the production and maximizing the net present value of the investment of oil field developments. However, the application of the optimization techniques in well placement developments is so complicated because many decision variables, objective functions, and constraints are involved in the well placement optimization case. Furthermore, many computational techniques; conventional and non-conventional, have been utilized to optimize well placement operations. This study displays the advancement of the optimization methods applied in the well placement. Subsequently, the study assorted the applied optimization methods, and it demonstrates the restriction and the range of implementation of each method to gain an appropriate degree of precision and simulation run time. Finally, the paper provides an inclusive review of the well placement optimization methods utilized in the petroleum engineering domain from conventional methods to modern artificial intelligence methods. [ABSTRACT FROM AUTHOR]

Geopolymer-based alternatives to concrete have been mentioned very frequently in the last decade as they conform the topical environmental saving efforts. Utilization of byproducts or waste material make them even more attractive from this point of view. However, the process of a design is not the same like in case of well known concretes which means. it might be difficult to reach the properties desired. This paper therefore aims at the optimization of a geopolymer composition to reach sufficient 7-day compressive strength. Being composed of metashale, water glass, siliceous sand and water, the mixture proposed is supposed to be used in floorings applications. As the optimization technique, the modified downhill simplex method was applied, being tailored to specifics of the mixture design. Proving its effectiveness, the demanded 7-day compressive strength (25.0 MPa) was achieved after the first optimization step (27.8 MPa, due to an combination with an empirical design that exploited authors' experience to set the initial simplex closer to the optimum zone. In the end of the research presented, analytical formulas of the composition were proposed that are supposed to guarantee that the required 7-day compressive strength will be achieved. [ABSTRACT FROM AUTHOR]

This paper presents a spreadsheet-based mixed-integer nonlinear programming (MINLP) model for cost-optimal construction project scheduling. The model was developed in an MS Excel spreadsheet containing all the input parameters necessary for optimization. The OpenSolver add-in is used to solve the scheduling problem. OpenSolver can solve the optimization task by directly using the computing power of the end user's computer or by converting it into an algebraic modeling language formulation, which is thereupon deployed on an online-based optimization service - NEOS (Network-Enabled Optimization System). With the NEOS server, the end-user can solve more complex problems in less time as they are processed by a supercomputer. The model capabilities are presented on a literature example of a construction project. The model can process project networks with generalized precedence relationships with corresponding lead/lag times. The results obtained by the solver automatically generate a Gantt chart for a cost-optimal construction schedule. [ABSTRACT FROM AUTHOR]

The traditional fixed topology of transmission networks is prone to power congestions, which can cause load curtailment and reduce the integrations of wind power, leading to more dispatching of conventional generators and higher overall dispatch cost. This can be overcome by improving the operation flexibility of transmission networks to enhance wind integration level. This is achieved in this paper by employing the network topology optimization technique to optimize network topology by flexibly altering the connections of lines and busbars. The proposed evaluation framework is generic and applicable on any network. Case studies performed on the IEEE 24-bus test system show that the proposed technique is effective in reducing the three costs considered here, namely the load curtailment cost, generator dispatch cost and wind curtailment cost. [ABSTRACT FROM AUTHOR]

In this modern world, human life has become increasingly reliant on electricity, which serves to be one of the basic needs of human to lead a normal life. As the usage of electricity has increased over the years, the consumers are very much concerned about its consumption rate and the electricity bill generated out of it. Hence monitoring the consumption rate stands first which is very trivial and challenging by designing an efficient energy management system. This paper outlines the survey on energy management systems implemented with artificial intelligence techniques towards finding a viable solution to the aforementioned issue. This study elucidates how artificial intelligent systems are incorporated in different energy management systems to match demand and supply. A comparative analysis of different intelligent techniques with optimization goals, issues and solutions, applied to the energy management systems for effective functioning is also presented. Finally, Home Energy Management System (HEMS) using Binary Particle Swarm Optimization Algorithm (BPSO) is presented. 26% reduction in the daily bill with optimization of HVAC and non-interruptible appliances was attained. Due to the interrupted supply of energy sources, effective storage model is determined to be an alternate viable option owing to technological advancement and capacity of ensuring excellent grid services. Future directions in terms of developing hybrid systems using hybrid energy sources and intelligent systems are also suggested. [ABSTRACT FROM AUTHOR]

Teaching-Learning Based Optimization (TLBO) approach is widely accepted in numerous engineering applications. Despite the similarity of this algorithm to other Meta-heuristic techniques in terms of employing a set of solution and stochastic nature, the inspiration of algorithm is unique. This paper aims at discussing the position of TLBO within the do-main of Nature-Inspired Algorithms (NIA). The effectiveness of this simple and robust paradigm in the fields of engineering and sciences is gaining popularity rapidly due to its less complex approach in tuning with the algorithm and other common parameters like population size, number of iterations and stopping criteria, etc. The paper also highlights the development, mechanism, mathematical formulation and algorithm of TLBO. Furthermore, it is concluded that TLBO is a promising variant in the class of stochastic methods that can be extended to find solutions of challenging real-world problems. The review is expected to be useful for the freshers in research area of Optimization techniques. [ABSTRACT FROM AUTHOR]

The development of industrial/agro based composites is a big challenge for engineers and scientists. Much research work is underway to develop metal matrix composites (MMC) and fibre reinforced polymer composites (FRP) for advanced engineering applications. The industry is seeking improvements in these assets. This paper focuses on the review of various composites produced using industrial-agricultural waste as catalysts and the consequences of this strengthening physical and biological environments. Research in this area has shown that the use of this industrial-agricultural waste as a reinforcing material has facilitated the complete operation of the improved composites and helped to reduce the cost of recycling. FRP Composites using agro based materials are developed using hand layup process and tested for Tensile, Flexural and Impact strengths with appropriate ASTM standards. Characterization using Taguchi optimization technique has suggested the use of jute fibre with fibre weight fraction of 10% and poly lactic acid (PLA) as matrix material to be the best combination among the test specimens. The concluding section of the paper discusses the potential uses of this waste as potential engineering materials to strengthen future research work. [ABSTRACT FROM AUTHOR]

The quantum and reversible circuits represent a promising replacement of conventional computers in many applications in the future. The transition between conventional computing and this alternative is indeed a huge paradigm shift. During the transition, embedding of conventional Boolean logic functions and expressions within quantum and reversible logic circuits has to be considered, because conventional computing is generally based on Boolean algebra. This embedding comes with a serious drawback of additional increase in the problem complexity represented by additional constant inputs, the so-called ancillary lines. An algorithmic optimization technique is suggested in this paper to reduce the number of these ancillary lines. With this algorithm embedding of Boolean expressions within reversible/quantum circuits comes out with minimal increase in circuit cost and gate count in the optimized circuits. [ABSTRACT FROM AUTHOR]

The paper is devoted to the consideration of the possibilities of managing complex energy systems. An approach related to the optimal planning of various types of energy services is shown. The paper proposes to apply approaches based on queuing systems. The divisions that will manage the energy flows in the distribution power system are considered from the point of view of using multiple flows of homogeneous events. The considered approaches will be used in the course of the formation of a simulation modeling scheme and optimization of parameters in the power system. The possibilities of energy monitoring are considered. When choosing indicators, optimization modeling is used. Further, expert analysis is seen as a very effective approach when analyzing a variety of promising options. As a result, the results are presented that demonstrate the effectiveness of the selected indicators. [ABSTRACT FROM AUTHOR]

In order to solve the ill-posed problem in super-resolution image reconstruction, this paper proposes an adaptive regularization way use sparse representation. We build a new L1/2 non-convex optimization model and apply reweighted L2 Norm for the adaptive algorithm in this paper. Experimental results show the significant effect in denoising and preserving edge details. It outperforms some traditional methods in the value of peak signal to noise ratio and structural similarity [ABSTRACT FROM AUTHOR]

Cloud computing is an emerging technology, in which users demand the computing resources such as software, storage, etc based on pay-per-use services. Since cloud computing is widely expanding and there is also an increase in the number of users. Therefore, energy consumption and resource utilization are serious concerns in complex cloud systems. To this end, Virtual Machine (VM) placement or virtualization plays an important role in allocating the resources and power to their users. For efficient allocation, multiple factors such as migration overhead, Service Legal Violation (SLA), number of migrations, etc. has been considered. Since the problem is challenging, several optimization algorithms have been considered to solve this issue and have been implemented using different methods. Therefore, this paper presents a review to discuss the role of optimization techniques for VM migration and allocation. The current paper has been discussed and a review of existing techniques post-2015-2022 has been presented. Further, a detailed comparison has been made to illustrate the important factors utilized in the past. [ABSTRACT FROM AUTHOR]

In this Research paper removal of faults associated in real life situation related to 66KV substations at Guru Gobind Singh Super Thermal Power Plant situated at Ropar. The power system data is taken at different time periods under various fault conditions and voltage sag/swell both are mitigated from the real time data by using D-STATCOM. When Grey Wolf Optimization technique is associated with D-STATCOM, it has shown more efficient results for voltage sag/swell. Later, a FACT controller device called UPQC has been used to overcome the same problem of voltage fluctuation in distribution system. The results have shown that UPQC is more efficient in comparison to D-STATCOM and GWO as values obtained through MATLAB SIMULINK in case of UPQC are closer to normal values. [ABSTRACT FROM AUTHOR]

In this paper, we highlight the problems related to UN SDG number 7 (providing affordable, reliable and sustainable energy) by analyzing the current infrastructure that provides uninterrupted reliable energy supply to technologically isolated and hard-to-reach territories in the Russian Federation. We explain the different components of Hybrid Energy Complexes that include various elements of renewable energy sources, we analyze the factors influencing the efficiency of these complexes and we optimize the composition of these complexes by using a mathematical simulation program that predicts various output indicators. By comparing our estimates with actual data in Ust-Kamchatsk, we are able to highlight the accuracy of our program as well as make conclusions on how its future implementation may have a positive effect on both ecological and economic factors. [ABSTRACT FROM AUTHOR]

Barrages on watercourses are usually designed as multi-purpose structures that were built to navigate rivers for water transport, protect the area from flooding, enable water withdrawals and utilize the hydropower potential. In the layout of the low head barrage a run-of-river hydropower plants (HPPs) are usually designed. The efficiency of operation of HPPs is significantly affected by the barrage layout and by the velocity conditions in the intake structure. The efficiency of HPPs can be adversely affected by inappropriate hydrodynamic design of the individual elements, which include the coarse screens and the submerged wall as protection against coarse debris, the sill at the entry to the intake structure to exclude the bed material, the intake channel and the fine screens. If these elements are not properly designed, high pressure losses are generated, leading to a reduction in the efficiency of the HPP. In addition to pressure losses in the intake structure, the homogeneity of the velocity field in the inlet profile upstream of the turbines has been shown to affect the efficiency of the HPP. In the case of an inhomogeneous velocity field, flow disturbances occur which have a demonstrable impact on the efficiency of the turbines. Nowadays, the method of physical and mathematical modelling is often used for the hydraulic design of the suitable shape of the intake structure as well as other HPP elements. The present paper describes the use of 3D mathematical modelling for the optimisation of the intake structure of a low head HPP with respect to the homogeneity of the velocity field in the control profile upstream of the turbine inlet. There are a number of objective criteria for evaluating the homogeneity of the velocity field, which have been validated by measurements on real HPPs. The procedure is verified for the case of the HPP intake of the planned Děčín barrage on the Elbe River. Optimal velocity conditions were ensured by the use of guide pillars in the profile of coarse screens and entry sill. The resulting optimized shape was verified using the physical model. Currently, there are many HPPs at existing barrages with unsatisfactory hydraulic conditions in the intake area. The described technical solution for the application of guide pillars in the intake has minimal impacts on the architectural character of the barrage and can therefore be used to increase the efficiency of historic hydropower plants with high cultural value. The research was conducted as part of project no. DG18P02OVV004 entitled "Documentation and presentation of technical cultural heritage along the Elbe-Vltava Waterway" within a support programme for applied research and experimental development of national and cultural identity, funded by the Ministry of Culture of the Czech Republic. [ABSTRACT FROM AUTHOR]

Optimization of gas lift plays a substantial role in production and maximizing the net present value of the investment of oil field projects. However, the application of the optimization techniques in gas lift project is so complex because many decision variables, objective functions and constraints are involved in the gas lift optimization problem. In addition, many computational ways; traditional and modern, have been employed to optimize gas lift processes. This research aims to present the developing of the optimization techniques applied in the gas lift. Accordingly, the research classifies the applied optimization techniques, and it presents the limitations and the range of applications of each one to get an acceptable level of accuracy and simulation run time. Finally, the paper provides a comprehensive review of the gas lift optimization techniques applied in the petroleum industry range from traditional method to the recent artificial intelligence techniques. [ABSTRACT FROM AUTHOR]

This paper attempts to compare the two socio-inspired based metaheuristics algorithms which were introduced recently. These metaheuristics optimizations and search techniques "Election algorithm, and Election Campaign Optimization Algorithm" were inspired by socio-political processes of human ideologies. This paper gives an overview of these algorithms with respect to their control parameters, components, intensification and diversification as well as their applications. It also makes a simple comparison between the two metaheuristics algorithms in terms of quality of solution, ease of finding correct control parameters and further explored their strengths and limitations as well as their superiority over other metaheuristics algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the latest work in the field of optimization of modern power system systems networks. Optimization is an efficient tool and an easy way to solve the complex power system problems. Improved performance, accurate information, greater adaptability, performance surveillance and accounting results in complicated problems are the advantages associated with optimisation techniques. The power systems problems include in this paper are automatic generation control problem, congestion management and transmission expansion planning, economic dispatch problem, unit commitment, state estimation and reactive power planning. [ABSTRACT FROM AUTHOR]

The research paper was aimed to optimize the routes of transporting yogurt milk from Annashuwa Company, Sharada Phase II Kano, to expedite the distribution route led to the reduction of operating cost that frequently occurs due to the traffic congestion in some selected areas within Kano city, Nigeria. To reduce the cost of transportation from the company to the destinations. A Mathematical Optimization Model i.e., Vogel approximation was employed to ease the cost of distributing the product to some selected destinations in the state. [ABSTRACT FROM AUTHOR]

Traditional file systems, while fundamental in data management, often grapple with the demands of ever-evolving data workloads, varying access patterns, and escalating volumes of information. In response to these challenges, the integration of artificial intelligence (AI) and machine learning has emerged as a transformative approach to optimize file systems. This paper outlines the importance of file system optimisation in the contemporary data landscape and then provides potential solutions using AI algorithms. AI-driven file systems optimization has delivered substantial performance improvements, characterized by enhanced Input/Output Operations Per Second (IOPS), reduced latency, and increased throughput. Artificial intelligence-driven solutions in cloud storage environments address scalability and data management challenges associated with cloud storage, extending the applicability of AI techniques. [ABSTRACT FROM AUTHOR]

Increasing load in a traditional transmission system has led to the increase of current flow which also increases the I2R loss leading to monetary losses in the power system community. This will also affect the economic status of the utility which may require the utility to take possible appropriate remedial action. Thus, a compensation process can be performed to alleviate the high current flow which in turn reduces the monetary losses that can be experienced by the utility. This paper presents Multi Load Variability for Composite Compensation in Loss Control Scheme. In this study a new optimization technique termed Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP) is proposed to optimize the location and sizing for composite compensation in power transmission system for loss control scheme. Several variations in the loading variability were conducted to evaluate the effect of reactive power changes which influence loss value in power system. The effectiveness of the proposed ICAMEP algorithm was compared with respect to the traditional evolutionary programming (EP) and artificial immune system (AIS) optimization techniques for performance comparisons. Results from ICAMEP outperformed AIS and EP in terms of achieving lower transmission loss through the validation process in the IEEE 30-Bus Reliability Test System (RTS). This will benefit power system operators and planners for their expansion planning efforts. [ABSTRACT FROM AUTHOR]

Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level. [ABSTRACT FROM AUTHOR]

Modern day military weapons are an integration of a several sub-systems and components. Each of these systems possesses distinct reliability characteristics, accompanied by diverse probability distributions that dictate their rates of failure. This paper focuses on the design of automated test equipment (ATE) and development of Graphical User Interface (GUI) for verification of weapon's Line Replaceable Units (LRUs) health status during functional ground testing of avionics systems. Functional ground testing of weapon LRUs through ATE is essential for ensuring the reliability and effectiveness of weapon. ATE is designed to perform Intermediate level periodic maintenance activity which includes the in-depth inspections, repairs, and replacements of faulty components. ATE is a computer-controlled system designed to perform tests automatically, eliminating human error and ensuring consistent results. It can perform a variety of tests, including electrical, functional, and environmental testing. A GUI is developed using Qt and C++ for verification of tests. The software supports multiple LRUs testing as well as individual LRU testing. The ethernet communication protocol between ATE and Unit Under Test (UUT)can be used to test multiple units at a time. During the functional ground testing process, the weapon is connected to the ATE and then ATE will send a series of signals to the weapon, simulating various operational scenarios. The ATE will monitor the weapon's LRU's response, comparing it to the expected results. If the LRU's response is outside the expected range, the ATE will flag the specific unit as faulty, and it will be removed from service. ATE will generate the test report of health of the constituent components. ATE provides a reliable and efficient way of identifying any issues with LRUs in the pre-flight phase and reduces the risk of malfunction. The use of ATE can increase testing efficiency and reduce costs, making it a valuable tool in modern warfare. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enhance microgrid resilience through the application of robust Model Predictive Control (RMPC). The primary objective is to minimize overall cost during prolonged main grid outages by optimizing resource allocation. The proposed methodology encompasses two essential phases: grid connected operation and resilient operation. In the former, optimal unit commitments, energy storage schedules, and adjustable loads are determined to inform the subsequent resilient operation phase. During resilient operation, the microgrid's capacity to fulfill local load demands amidst main grid disruptions is evaluated. A crucial element of this approach is the mitigation of uncertainties through robust optimization techniques, thereby guaranteeing the solution's resilience in the face of variations in load, non-dispatchable generation, and the timing of main grid interruptions. The resulting refined solution strikes a delicate equilibrium between economic efficiency and resilience, thereby facilitating seamless islanding with minimal disruptions. Through numerical simulations conducted within a test microgrid, this approach's effectiveness is validated, highlighting its potential to bolster microgrid resilience by strategically optimizing resource allocation, particularly in critical scenarios. [ABSTRACT FROM AUTHOR]