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Evolutionary algorithms (EAs) have been used extensively for the optimal design of water distribution networks (WDNs). There is evidence in the literature that search space reduction is highly effective. However, practical methods that do not introduce extra computational requirements are lacking. A dynamic search space reduction methodology is proposed to search the entire solution space without eliminating any part of the search space beforehand. The proposed methodology works on the information explored during the execution of the algorithm. Further, a self-adaptive penalty is suggested which is based on both flow and pressure deficits instead of only pressure deficit and is obtained using pressure dependent analysis. In this study, the methodology is demonstrated using a Genetic Algorithm (GA). The effectiveness of the methodology is demonstrated on the Ramnagar Network of Nagpur City, India and two benchmark problems from the literature. The proposed methodology resulted in a substantial reduction in the computational efforts and provided nine improved solutions as compared to the best solution available in the literature for one of the networks. The techniques proposed are generic and can be incorporated in other EAs. [ABSTRACT FROM AUTHOR]

The significance of air quality monitoring for analyzing the impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments ranging from expensive reference stations that provide accurate data to low-cost sensors that provide reasonable air quality measurements. In this research, we use a combination of sensors and monitors, which we call as hybrid instruments and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM2.5 concentration). Note that there can be several constraints such as those on budget, minimum number of reference stations to be placed, set of important regions where at least one sensor should be placed and so on. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and works on the principles of evolution. The second one is a greedy algorithm that selects locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM2.5 concentration, budget available, etc. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb. [ABSTRACT FROM AUTHOR]

Mathematical validation and comparison of coal milling operations of a horizontal ball tube mill and a vertical bowl mill located in two distinct coal based power plants in India was carried out. The coal mill model was developed and the unknown model coefficients were obtained numerically by genetic algorithm (GA). The input variables of mass flow rates of raw coal and air and temperature of inlet primary air were obtained from the on-site data. The output variables of mill current and mill outlet temperature as obtained by GA were compared with on-site data for the two mills and show good agreement. The ball tube mill requires less mass flow rate of primary air and raw coal per MW of power produced than the bowl mill. It contains larger mass of raw and pulverized coal but less grinding heat is generated in this mill. [ABSTRACT FROM AUTHOR]

In the technological development era, the solution to various complex problems is uncertain, and more attention should be paid for changing certain conditions. Therefore, a precise solution to a complex problem is critical. The traveling salesman problem (TSP) is often fuse for an outbreak of a better solution. Inspired by the successful genetic algorithm (GA) applications, this study proposes a new approach to improve the convergence rate by incorporating a unique feature, namely 'sub-tour division'. The new method consists of multiple zones of TSPs (i.e., active and inactive), which are used to sort and group the critical region for finding the solutions. To illustrate the performance of the new approach, the traveling distance between various cities in India is considered as a problem. The simulation findings show that the new approach provides a more accurate and robust solution to a complex problem than alternative methods. [ABSTRACT FROM AUTHOR]

Global warming is impacting almost every nation of the world and causing excessive socioeconomic damage to human civilization. India is presently the second most inhabited country on the planet and possesses the noteworthy potential to curb global greenhouse gas emissions. Most of the stakeholders of the global communities have signed the Paris treaty of 2015 to curtail the surface temperature rise. As the central government of India has announced its target to attain net zero-emission by the end of 2070, the electricity generation sector of the country needs to utilize renewable resources like wind energy rapidly. This paper focuses to optimize the wind energy generation cost in the Tirumala region of the country using the Genetic Algorithm and Particle Swarm intelligence concurrently. Tirumala is located in the area of Tirupati in the southern state of Andhra Pradesh. A relative analysis of the optimization outcomes validates the superiority of the Genetic Algorithm over the Binary Particle Swarm Optimization Algorithm for minimizing the wind energy generation cost. The application of the Genetic Algorithm has been proven to cut down the generation cost by up to 7.56% as compared to the usage of Binary Particle Swarm Optimization for similar terrain conditions and wind flow conditions in the Tirumala Area. [ABSTRACT FROM AUTHOR]

This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031. [ABSTRACT FROM AUTHOR]

The procurement of food grains from farmers and their transportation to regional level has become decisive due to increasing food demand and post-harvest losses in developing countries. To overcome these challenges, this paper attempts to develop a robust data-driven supply chain model for the efficient procurement of food grains in India. Following the data collected from three leading wheat producing Indian regions, a mixed-integer linear programming model is formulated for minimising total supply chain network costs and determining number and location of procurement centres. The NK Hybrid Genetic Algorithm (NKHGA) is employed to cluster the villages, along with a novel density-based approach to optimise the supply chain network. Sensitivity analysis indicates that policymakers should focus on creating an adequate number of procurement centres in each surplus state, well before the start of the harvesting season. The study is expected to benefit food grain supply chain stakeholders such as farmers, procurement agencies, logistics providers and government bodies in making an informed decision. [ABSTRACT FROM AUTHOR]

Here, a new naturally-inspired stochastic nonlinear joint and individual inversion technique for integrating direct current (DC) and magnetotelluric (MT) data interpretation-based simulation of a swarm intelligence combo with specific capabilities for exploitation of the variable weight particle swarm optimizer (vPSO) and exploration of the grey wolf optimizer (GWO), vPSOGWO, is used. They are particularly notable for their capacity for information exchange while hunting for food. Through synthetic MT and DC data contaminated with various sets of random noise, the applicability of the anticipated vPSOGWO algorithm based joint and individual inversion algorithm was assessed. The field examples, collected from diversified different geological terrains, including Digha (West Bengal), India; Sundar Pahari (Jharkhand), India; Puga Valley (Ladakh), India; New Brunswick, Canada; and South Central Australia, have shown the practical application of the proposed algorithm. Further, a Bayesian probability density function (bpdf) for estimating a mean global model and uncertainty assessment in posterior; and a histogram for model resolution assessment have also been created using 1000 inverted models. We examined the inverted outcomes and compared them with results from other cutting-edge methodologies, including the GWO, PSO, genetic algorithm (GA), Levenberg–Marquardt (LM), and ridge-regression (RR). Our findings showed that the current methodology is more effective than the GWO, PSO, GA, LM, and RR techniques at consistently improving the convergence of the global minimum. In contrast to earlier approaches, the current cutting-edge strategy vPSOGWO offers an improved resolution of an additional significant crustal thickness of about 65.68 ± 1.96 km over the Puga Valley, in which the inverted crustal thickness determined by vPSOGWO agrees well with the published crustal thickness over the Puga Valley. The new technology brings simulations closer to genuine models by significantly reducing uncertainty and enhancing model resolution. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) in conjugation with genetic algorithms (GAs) have been demonstrated to be an effective tool for system modelling and optimization in a variety of applications. The current communique is about assessing the capacity of ANN to predict investment on cattle till age at first calving (AFC) and milk production based on the data of 340 Vrindavani crossbreed cattle developed at the ICAR-Indian Veterinary Research Institute in Izatnagar, India. Three distinct artificial neural network (ANN) algorithms, namely, Levenberg–Marquardt (LM), Bayesian regularization (BR), and gradient descent momentum with adaptive learning rate backpropagation (GDX) were used to train the ANN infrastructure for determining milk production and investment based on body weight and AFC as input variables. The results showed that BR with 2 hidden layer neurons showed excellent prediction ability (R2 = 0.999, MSE < 10−6) and was therefore used as an objective function by GA for optimization. The optimized results revealed that higher milk production is achievable at lower investment if the age at first calving is 768 days with a body weight of ∼281 kg. The information generated by this investigation will aid in ensuring food security in terms of higher milk production while making the dairy business more sustainable and profitable for the farmers. [ABSTRACT FROM AUTHOR]

Forecasting of sediment load (SL) is essential for reservoir operations, design of water resource structures, risk management, water resource planning and for preventing natural disasters in the river basin systems. Direct measurement of SL is difficult, labour intensive, and expensive. The development of an accurate and reliable model for forecasting the SL is required. Sediment transport is highly non-linear and is influenced by a variety of factors. Forecasting of the SL using various conventional methods is not highly accurate because of the association of various complex phenomena. In this study, major key factors such as rock type (RT), relief (R), rainfall (RF), water discharge (WD), temperature (T), catchment area (CA), and SL are recognized in developing the one-step-ahead SL forecasting model in the Mahanadi River (MR), which is among India's largest rivers. Artificial neural networks (ANN) in conjunction with multi-objective genetic algorithm (ANN-MOGA)-based forecasting models were developed for forecasting the SL in the MR. The ANN-MOGA model was employed to optimize the two competing objective functions (bias and error variance) with simultaneous optimization of all associated ANN parameters. The performances of the proposed novel model were finally compared to other existing methods to verify the forecasting capability of the model. The ANN-MOGA model improved the performance by 12.81% and 10.19% compared to traditional AR and MAR regression models, respectively. The results suggested that hybrid ANN-MOGA models outperform traditional autoregressive and multivariate autoregressive forecasting models. Overall, hybrid ANN-MOGA intelligent techniques are recommended for the forecasting of SL in rivers because of their relatively better performance as compared to other existing models and simplicity of application. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc. Design/methodology/approach: In this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules. Findings: The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models. Practical implications: The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place. Originality/value: The proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals. [ABSTRACT FROM AUTHOR]

Urban features can be analysed using high-resolution satellite images. Satellites procure panchromatic images at a spatial resolution even better than 1 m. However, it cannot cover the large area of a city in one scene. Therefore, satellite images from different dates and times must be mosaicked to study the urban region as a whole. Efficient and seamless mosaicking requires relative radiometric normalization (RRN), which generally is an iterative process. In this paper, an approach using the genetic algorithm has been developed for normalization in a single step. The algorithm has been tested on Jaipur city in India. Results produced for the mosaic using genetic algorithm provide visually seamless mosaic over the study region with significant improvement of the RMSE among the images’ overlap region. The algorithm has been compared against the mean-standard deviation (MS) and histogram matching (HM) methods of RRN with average RMSE improvement of 40% and 38%, respectively [ABSTRACT FROM AUTHOR]

India is at the moment the fifth largest economy and sustains the second biggest population in the world. To uphold its phenomenal economic growth, it would need cheap and reliable energy resources in the coming decades like any other nation. Moreover, the central government of India has announced its goal to attain carbon neutrality by 2070 following the pledges of the Paris treaty of 2015. To realize such a great aim, India needs to use its environment-friendly energy generation technologies rapidly and efficiently. Wind energy can help coastal states of India to generate electricity with a minimal carbon footprint and boost their economic progress. This current paper focuses on designing a wind farm in the western part of India using artificial intelligence methodologies. An innovative technique to escalate the efficiency of the genetic algorithm has been proposed to augment the economic output of the wind farm. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a stochastic Knapsack Problem (KP) based mathematical model for small-scale vegetable sellers in India and solved it by an advanced Genetic Algorithm. The knapsack problem considered here is a bounded one, where vegetables are the objects. In this model, we have assumed that different available vegetables (objects) have different weights (that are available), purchase costs, and profits. The maximum weight of vegetables that can be transported by a seller is limited by the carrying capacity of the vegetable carrier and the business capital of the seller is also limited. The aim of the proposed mathematical model is to maximize the total profit of the loaded/traded items, with a set of predefined constraints on the part of the vegetable seller or retailer. This problem has been solved in a Type-2 fuzzy environment and the Critical Value (CV) reduction method is utilized to defuzzify the objective value. We have projected an improved genetic algorithm based approach, where we have incorporated two features, namely refinement and immigration. We have initially considered benchmark instances and subsequently some redefined cases for experimentation. Moreover, we have solved some randomly generated proposed KP instances in Type-2 fuzzy environment. [ABSTRACT FROM AUTHOR]

Digitalization accumulates data in a short period. Smart agriculture for crop identification for cultivation is a common problem in agriculture for agronomists. The generated data due to digitalization does not provide any useful information unless some meaningful information is retrieved from it. Therefore from the existing information system, prediction of decision for unseen associations of attribute values is of challenging. This paper presents a model that hybridizes a fuzzy rough set, real-coded genetic algorithm, and linear regression. The model works in two phases. In the initial phase, the fuzzy rough set is used to remove superfluous attributes whereas, in the second phase, a real-coded genetic algorithm is used to predict the decision values of unseen instances by making use of linear regression. The proposed model is analyzed for its viability using agricultural information system obtained from Krishi Vigyan Kendra of Thiruvannamalai district of Tamilnadu, India. Further, the accuracy of the proposed model is compared with existing techniques. [ABSTRACT FROM AUTHOR]

Rivers are the agents on earth and act as the main pathways for transporting the continental weathered materials into the sea. The estimation of suspended sediment yield (SSY) is important in the design, planning and management of water resources. The SSY depends on many factors and their interrelationships, which are very nonlinear and complex. The traditional approaches are unable to solve these complex nonlear processes of SSY. Thus, the development of a reliable and accurate model for estimating the SSY is essential. The goal of this research was to develop a single hybrid artificial intelligence model, which is a hybridization of the artificial neural network (ANN) and genetic algorithm (GA) (ANN-GA) for the estimation of SSY in the Mahanadi River (MR), India, by combining data from 11-gauge stations into a single hybrid generalized model and applying it to every gauging station for estimating the SSY. All parameters of the ANN model were optimized automatically and simultaneously using GA to estimate the SSY. The proposed model was developed considering the temporal monthly hydro-climatic data, such as temperature (T), rainfall (RF), water discharge (Q) and SSY and spatial data, including the rock type (RT), catchment area (CA) and relief (R), of all 11 gauging stations in the MR. The performances of the conventional sediment rating curve (SRC), ANN and multiple linear regression (MLR) were compared with the hybrid ANN-GA model. It was noticed that the ANN-GA model provided with greatest coefficient of correlation (0.8710) and lowest root mean square error (0.0088) values among all comparative SRC, ANN and MLR. Thus, the proposed ANN-GA is most appropriate model compared to other examined models for estimating SSY in the MR Basin, India, particularly at the Tikarapara measuring station. If no measures of SSY are available in the MR, then the modelling approach could be used to estimate SSY at ungauged or gauge stations in the MR Basin. [ABSTRACT FROM AUTHOR]

Copious and accessible sun radiation creates an attractive system for converting solar energy into electrical energy. The solar power plants are installed in remote locations and hence needs monitoring and controlling system from remotely. A computerized remote solar plant monitoring system with an enormous range of solar Photovoltaic (PV) exploitation needs well-developed systems with Web-based interfaces. This research work focuses an inter-disciplinary field of renewable solar power plants located in the southern region of India, integrating with recent digital technology of Internet of Things (IoT) which supports to monitor real-time data of solar irradiation (G), temperature (T), clearness index (Kt), panel voltage (Vpv), current (Ipv), peak power (Pmp), and tilt angle of solar panel (β°) and also to control the optimum inclination angle (β°opt) of the solar panel for maximizing of PV power using Genetic Algorithm (GA). An integrated solar power plant with IoT is developed with hardware setup and virtually tested for enhancing the tracking behavior of the proposed system. The IoT-based control of solar power plant significantly improves the performance of monitoring and maintenance of the power plant parameters with cost-effectiveness. [ABSTRACT FROM AUTHOR]

In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as "Extra Tree Classifier" and "Logistic Regression" to predict the churn customers. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are among the most revolutionary breakthroughs by the context of decarbonization of road transport. The adoption of EVs has drawn increasing attention and is currently considered a potential route towards sustainable transportation. Businesses approaching the EV market must establish lucrative business models that overcome the adoption obstacles for EVs while assuring long-term growth. The intent of the current research exploration is to create a cutting-edge framework for EV enterprises that aims to maximize profit and save delivery time leading to high customer satisfaction. The volume discount strategy is used for profit escalation. The model presented in this paper is highly adaptable for a real-world problem as it incorporates uncertainty in the EV industry through generalized triangular neutrosophic numbers, an extension of fuzzy numbers. A modified version of the neutrosophic compromise programming approach is proposed in this paper. The results from this approach are validated using genetic algorithm. The proposed optimization-based framework is encapsulated in the form of study of a case from Bangalore city, in India. An in-depth exploration of two cases along with sensitivity analysis is carried out that helped in prioritizing some discrete customers and gave insights on which manufacturing site should be designated more focus. • Profit maximizing business model for electric vehicle (EV) merchants is proposed. • Uncertainty in EV industry is captured using fuzzy number. • A modified neutrosophic compromise programming technique is proposed. • Case study is considered involving particular case and sensitivity analysis. [ABSTRACT FROM AUTHOR]

In the hilly regions of India, hydro power potential is available, but it is not completely utilized for power generation till date due to the unavailability of proper electrical structure. Nine micro/mini hydro power plants have been developed in the bageswar district in Uttarakhand. The development of the optimized Mini-grid structure is an effective and reliable solution for the full utilization of these MHPs potentials. Although Ajit Singh et al. proposed some Mini-grid structures for this area, their work is not appropriate due to their calculation method of reliability. For the determination of reliability (failure and repair time), they have considered only the line length. However, its demographic situations such as elevation level of MHPs, availability of roads, the presence of obstacles like streams will also affect the failure and repair time. This paper proposes an optimized Mini-grid structure for interconnection of these nine MHPs and load substations. All the MHPs are spreads in two blocks, namely Kapkote and Bagheswar, so two substations (load canters) are considered. These MHPs and substations are mapped through the Google Earth software and the optimization data such as distances between MHPs and substations, their elevation levels, the highest and lowest elevation in the connecting path and the path profile are collected through it. The Mini-grid structure is optimized through the Genetic Algorithm using the MATLAB tool. The optimization is performed by making the cost function as an objective function considering the line lengths and reliability of the path for its parameters. [ABSTRACT FROM AUTHOR]

Agrivoltaic system is a symbiotic approach to minimize land use conflicts for energy-food production. This production is dynamically affected by environmental parameters. Hence, an efficient system design is needed by adopting a suitable optimization model. The objective of this study is to discuss the application of artificial neural network and genetic algorithm to an experimental 0.675 kWp agrivoltaic system with turmeric crops for optimum production. Turmeric is a medicinal crop used primarily as spices and traditional medicine. The input parameters for the model are solar radiation, temperature, humidity, and rainfall, while the output parameters are energy and food. After training, validation, and testing of the model, the error has been found as -0.03711 and -0.00494 for energy and food systems respectively. For system performance, the established model generates a minimum mean squared error and maximum regression correlation coefficient indicating a favourable relationship between measured and predicted values. The predicted energy and food production has been obtained at optimal conditions as 104.9097 kWh and 9.0955 kg, respectively. The performance indicators such as land equivalent ratio and payback period have been found as 1.73 and 9.49 respectively. Thus, the system is techno-economically viable and feasible worldwide satisfying the targets of sustainable development goals. [Display omitted] • Co-development of solar PV panels and crops on the same land increases land-use efficiency and total productivity. • Agrivoltaic potential with turmeric farms at CUTM, Odisha, India has been assessed. • Experimental data has been modeled and optimized through the ANN-GA algorithm for better analysis. • The system analysis meets the researcher's demands and allows many extensions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the use of satellite image processing and remote sensing approaches to determine the water-spread area and capacity of a drinking water reservoir in Poondi, near Chennai City, south India. Estimation of water-spread area and volume of the reservoirs using traditional methods like field surveys, acoustic surveys and hydrographic surveys is time-consuming, laborious and expensive. To overcome these problems, many researchers have used satellite images and the per-pixel (hard classification), the sub-pixel (soft classification) and the super resolution mapping approaches. The conventional hard and soft classification approaches do not give accurate results due to the presence of mixed pixels in the image scene of the periphery of the reservoir. In this work, a per-pixel algorithm (Maximum Likelihood), a sub-pixel algorithm (Fuzzy C Means) and a super resolution mapping algorithm (Genetic algorithm) are developed and the water-spread area is estimated for the reservoir using multi-date Landsat8 OLI images. The volume of the reservoir at different water levels are estimated using the water-spread area and the Trapezoidal formula. The capacity estimated from satellite image-derived area is compared with the capacity data obtained from the Poondi reservoir authority. The error in estimation due to the per-pixel approach is 16.74%, while it is 9.06% for the sub-pixel approach and a mere 3.25% for the super-resolution approach. Thus, the super-resolution mapping approach results in minimum error when compared to the sub-pixel approach which in turn gives lesser error result when comparing with the per-pixel approach. [ABSTRACT FROM AUTHOR]

This work assessed the effects of joint operation on the performance of multipurpose, multireservoir systems vis-à-vis standalone operation. The analysis used the multipurpose Pong and Bhakra reservoirs in northern India. Zone-based rule curves with or without hedging optimized using genetic algorithms were used for operating the reservoirs. The results showed that standalone configurations without hedging produced irrigation water reliability exceeding 90%, but the vulnerability indices (40% for Pong and 58% for Bhakra) were also high. Hedging tempered the standalone vulnerability; however, integrated operation also caused the vulnerability to reduce without hedging. Flood control was also enhanced during the flood season, with flood freeboard increasing by 12 and 3 m, respectively, at Bhakra and Pong reservoirs, relative to nonoptimized operation. Hydropower generation was lower than installed capacity at both reservoirs for both standalone and joint operations. The main conclusion of the study is that although hedging will moderate the vulnerability of standalone systems, the same outcome can be achieved without the deliberate water rationing of hedging if reservoirs are operated jointly. [ABSTRACT FROM AUTHOR]

Summary: Intelligent Transport System (ITS) involves a number of Information and Communication Technology (ICT) interventions for efficient road safety management. Over the last few decades, there has been a significant increase in the safety of Vulnerable Road Users (VRU's). This study aims to identify the most frequent, regularly occurring causes of road accidents and outline actions that can be used as a baseline for improving road traffic safety. The main contributions in this paper include the following: a graphical investigation for finding out the overall rate of road accidents (injuries and deaths) in India. Based on graphical analysis, major loopholes responsible for road accidents in India are summed up. This paper discusses various issues faced by already available pedestrian and vehicular safety techniques. After analysis of the available techniques and their related case studies in a systematic way, it is observed that they are not able to overcome the loopholes (Collision Occurrence, Traffic Congestion, Pedestrian Crossing alert, Red Light violation, and Non‐Propagation of Distress Signal) present in the current "Road Transport Management (RTM) System" of India. Finally, after deep analysis of already available techniques, a futuristic approach "Mesh‐Networking based Vehicle Ad‐Hoc System (MN‐VAS) using hybrid of Neuro‐Fuzzy and Genetic Algorithm based Load Balancing on Node" is proposed for the safety of VRU's. This hybrid approach has been discussed with domain experts for practical input, and we concluded that this might be a good option for better RTM System in India. [ABSTRACT FROM AUTHOR]

Carrier Aggregation (CA) technology, introduced in 3GPP release 10, aims at increasing Spectral Efficiency (SE) only, which may lead to huge undesirable circuit power consumption. Hence, not only SE, but also Energy Efficiency (EE), particularly the tradeoff between both the parameters, is crucial in the design of CA systems. In this work, a novel approach, using Genetic Algorithm (GA) is proposed to solve the SE-EE tradeoff problem for a 5 G heterogeneous network with intra-band contiguous Carrier Aggregation (CA). Complete physical layer simulation is performed for generating the CA waveform, with parameters set in accordance with practical deployment scenarios, as specified in 3GPP TS 38.104. Carrier aggregated waveform is generated in 5 G New Radio (NR) frequency band FR1, using frequency bands in India which are currently not allocated for licenced usage. From simulations, it is observed that the proposed technique ensures that neither SE nor EE is seriously degraded for improving the other, for any given value of transmit power. The proposed technique, which performs Multi Objective Optimisation (MOO) using SPEA-2 GA outperforms Single Objective Optimisation (SOO) methods providing a maximum of 37% improvement in SE and 43% improvement in EE. [ABSTRACT FROM AUTHOR]

With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. [ABSTRACT FROM AUTHOR]

Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models are common techniques that are widely used in analysing and forecasting stationary and seasonal time series data. The three essential steps involved to construct ARIMA are identification, estimation, and checking the validity of the model. The most critical step followed in constructing the ARIMA model is model identification. However, overcoming the difficult local optima problem for both ARIMA and SARIMA is still challenging as there is no appropriate method to solve it. In this paper, the proposed parallel GA-SARIMA model is used to solve the problem of local optima, where the genetic algorithm (GA) is used at the initial stage to identify the order and estimation of the parameters for SARIMA. The National Climate Data Centre (NCDC) time series dataset is used for testing the efficiency of the final parallel GA-SARIMA model to forecast the mean temperature of India from 2000 to 2017. The GA algorithm is successfully implemented to solve the optimization problems by introducing better solutions suitable for SARIMA models. The results of the study showed that the implementation of the combined approach of parallel GA and SARIMA enhances the prediction accuracy of the model. The parallel GA-SARIMA method is particularly robust, faster and performs better than sequential SARIMA models in terms of running time and cost function values. [ABSTRACT FROM AUTHOR]

Finding optimal policies for real-life reservoir systems operation (RSO) is a challenging task as the available analytical methods cannot handle the arbitrary functions of the problem. Most of the methods employed are numerical or iterative type and are computer dependent. Since the computer resources in terms of memory and CPU time are limited efficient algorithms are necessary to deal with the RSO problems. In this paper we present a Genetic Algorithms (GA) optimized rule curve (RC) model for monthly operation of a multipurpose reservoir which maximizes hydropower produced while meeting the irrigation demands with a given reliability. Instead of the usual single target storage for each period the proposed model considers three sets of target storages, namely dry, normal, and wet storages, based on the beginning of the period storage level. The reservoir considered is Bhadra Multipurpose Reservoir, in the state of Karnataka, India, which supplies water to irrigation fields through two canals while generating hydropower with turbines installed at each of the canal heads and at the river bed. Optimization ability and robustness of GA-RC approach are ascertained through simulation with a different inflow sequence for which global optimum is computed using Dynamic Programming. Further, a 15 year real-time simulation of the reservoir using historical inflows and demands showed significant improvement in the benefit, i.e. power produced, without compromising on the irrigation demands throughout the operation period. [ABSTRACT FROM AUTHOR]

The arrival of nCoV virus took the existence of human race in the toss, thus to slow down the spread of the virus Indian Government imposed a nationwide lockdown. In this paper, we aim to find the liaison between the impacts of lockdown on slowing down the spread of the virus along with the unprecedented blow to the small cap companies enlisted in the stock market. The impact of lockdown model has been assessed by proposing a differential equation based mathematical modelling (SIQR Modelling). Along with that, the stock market effect has been analyzed using a proposed Bi-Directional LSTM model optimized using Genetic Algorithm. In this present research, we have optimized the time-stamp window using genetic algorithm, before passing through the LSTM architecture. The novelty of the paper lies in the bi-fold approach to assess the chaos of the stock market, under 2 conditional scenarios i.e. with and without the lockdown. [ABSTRACT FROM AUTHOR]

Rivers are an integral part of the hydrological cycle and are the major geological agents which erode the continents and transport water and sediments to the oceans. Thus rivers act an important link between continents and oceans for the mass balance. Estimating the suspended sediment yield is one of the crucial aims in the field of managing, designing and planning of any river system or reservoir. To determine the suspended sediment yield in a river basin demands more labour or it is more costly when automatic sampling devices are used. The number of variables and the inter-relationship existing among them influence the suspended sediment yield and the nature of these inter-relationships is neither linear nor simple. Unfortunately, it is a difficult task to determine the suspended sediment yield when traditional mathematical models are used as complex variables and processes are involved. The major key factors, such as basin geology (rock type), relief, rainfall, temperature, water discharge and catchment area that affect sediment yield, are used as inputs to develop the model for predicting the suspended sediment yield in the Mahanadi River. In this paper, a multi-objective genetic algorithm for artificial neural network (MOGA-ANN)-based approach is used for predicting the suspended sediment yield. The MOGA assists ANN to minimize the two competing objectives, i.e. mean error and variance simultaneously. Thus in this study, a hybrid artificial intelligence-based method, MOGA-ANN model, is developed using the hydro-geological-climatic factors where all parameters associated with the ANN models are optimized simultaneously using MOGAs to estimate the suspended sediment yield in the Mahanadi River basin. The ANN's parameters are optimized globally by the MOGA to accurate estimation. The study has been carried out to develop MOGA-ANN for estimating the suspended sediment load using 20-year data at the Tikarapara gauging station which is the last downstream station in the Mahanadi River. The MOGA-ANN model provided the root mean square error 0.0281, correlation coefficient (r) 0.966 and efficiency factor 0.919 during the testing phase. The results suggested that the hybrid MOGA-ANN model exhibited satisfactory performance with an accuracy of 96.41%. [ABSTRACT FROM AUTHOR]

The power sector of India is in a huge catastrophe in satisfying the energy requirement of the public due to incessant exhaustion of fossil fuels. The nonstop exhaustion of fossil fuels, rising power needs and increasing production cost of power requires economic operation at the generation side and economic utilization at the consumer side. Economic dispatch is the process of determining the optimal power output from 'n' number of generators to meet the demand at low cost subject to certain constraints. Economic dispatch ensures the optimal generation of power at low cost from thermal power plants. The mathematical formulation of economic dispatch problems is usually done by the piecewise quadratic fitness function. This article compares the results generated from various techniques such as Lambda Iteration (LI) method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Quantum Particle Swarm Optimization (QPSO) and Shuffled Frog Leaping Approach (SFLA). LI method is a traditional method of solving economic load dispatch which works on the concept of equal incremental cost (λ). GA works on Darwin's theory of evolution, where the population of individual solutions is modified repeatedly to obtain the optimal solution in the population. PSO is derived from the concept of swarm intelligence, where the best solution is found using the values of personal best and global best in the population. QPSO is basically derived from the PSO. SLFA is obtained from the concept of food- frogs used to find an accurate solution to our power system problem. In this paper, the best fuel cost and execution time was found from QPSO, SFLA compared with LI, GA and PSO methods. These approaches are applied for three and thirteen generator system and the convergence characteristics, heftiness was explored through comparisons from different approaches discussed earlier. The results are hopeful and it suggests that shuffled frog leaping algorithm is very effectual in terms of both the minimized fuel cost obtained and the execution time. [ABSTRACT FROM AUTHOR]

Wind Farm Layout Optimization Problem (WFLOP) is a critical issue when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large hypothetical offshore wind farms using real wind data. GA mimics the natural selection process observed in nature, which is the survival of the fittest. The study site is the Palk Strait, located between India and Sri Lanka. This site is a potential hotspot of offshore wind in India. A modified Jensen wake model is used to calculate the wake losses. GA is used to produce optimal layouts for four different wind farms at the specified site. We use two different optimization approaches: one where the number of turbines is kept the same as the thumb rule layout and another where the number of turbines is allowed to vary. The results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) compared to the thumb rule due to the reduction in wake losses. Optimized layouts where both the number and locations of turbines are allowed to vary produce better results in terms of efficiency and cost but also leads to lower installed capacity and power generation. Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial wind resources are almost saturated, and offshore wind is the new frontier. This study can play an important role while taking the first steps towards the expansion of offshore wind in India. [ABSTRACT FROM AUTHOR]

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles realtime Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem's computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The "walk mode" and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner's capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper. [ABSTRACT FROM AUTHOR]

Internet made a drastic change in the way data are collected. There has been huge and huge collections of data. All these data serve no purpose unless some useful information is mined from it. Prediction of future instances will be major research problem. In this work, we adapt a rough and real coded genetic algorithm-based prediction system for prediction of future instances. We adapt rough set in this work because of uncertainties present in the data. Additionally, it is used to eliminate the unwanted attributes. Real coded genetic algorithm is used to predict the values for the unknown instances by making use of multiple linear regression. The model is experimented over agriculture data obtained from Tiruvannamalai district of Tamil Nadu. The experimental results show the viability of proposed research. [ABSTRACT FROM AUTHOR]

Context: Computer-aided diagnosis (CAD) combining mammographic features from cranio-caudal (CC) and medio-lateral-oblique (MLO) views improve the diagnostic performance of breast cancer. This could help doctors incorrect diagnosis at the earlier stage thereby reducing mortality.Aim: The aim of this study is to propose a breast cancer diagnostic technique for improving the diagnostic accuracy and reducing the false positive rate by fusing mammographic features from CC and MLO views.Settings and Design: The MLO and CC view mammograms of same patients must be used to extract k-Gabor features and then fused to form a single feature vector.Subjects and Methods: Mammograms from the digital database for screening mammography (DDSM) and INbreast datasets are collected. k-Gabor features extracted from both MLO and CC view mammograms are fused serially and reduced by principal component analysis (PCA) or genetic algorithm. The reduced features are classified using a multi-layer perceptron feed forward neural network with backpropagation learning algorithm.Statistical Analysis Used: Various relevant performance metrics such as accuracy, sensitivity, specificity, discriminant power, Mathews correlation coefficient (MCC), F1 score and Kappa are used to analyze the classification results.Results: The accuracy, sensitivity, specificity, discriminant power, MCC, F1 score, and Kappa obtained as 92.5%, 93%, 91.8%, 1.198, 0.845, 0.936, and 0.845, respectively, for DDSM. For INbreast, the above specified metrics are 87.5%, 90.9%, 85.7%, 0.980, 0.741, 0.833, and 0.734, respectively. The results show 4.4%, 4.3%, and 9.4% improvements in accuracy, sensitivity, and specificity, respectively, compared to the previous works.Conclusions: Detailed analysis of the results implies that the serial fusion of k-Gabor features extracted from MLO and CC views with PCA reduction in CAD significantly improves the diagnostic performance. [ABSTRACT FROM AUTHOR]

• Evaluated the performance of SWAT in reproducing daily-scale reservoir inflow. • HEC-ResSim derived reservoir outflow estimates are reliable for baseline scenario. • Proposed 10-daily scale E-flow estimation approach is highly reliable. • EPRC and SPRC scenarios based rule curves have more practical implications. • Proposed SWAT-HEC-ResSim-GA approach could effectively govern reservoir operation. The complex hydrological connectivity of integrated river-reservoir catchment command regions in conjunction with abrupt societal development and population growth impose adverse water availability scenario in the global reservoirs. In this context, to formulate optimal reservoir operation policies, numerous reservoir simulation and optimization models are currently on board. However, lack of combined hydrologic and hydraulic conceptualizations in the existing reservoir operation framework fetches considerable uncertainty; thereby leading to formulation of erroneous reservoir operation rule. This study proposed an integrated Soil and Water Assessment Tool (SWAT) based hydrologic, HEC-ResSim based hydraulic, and genetic algorithm (GA) based optimization framework (SWAT-HEC-ResSim-GA) for formulating the optimal reservoir operation rule of the Kangsabati reservoir located in the eastern India. Unlike the existing reservoir operation rules, the present study incorporates a 10-daily-scale environmental flow estimation approach to quantify the water demand. The proposed reservoir operation framework generated rule curve based HEC-ResSim simulation revealed improved reservoir performance with reservoir time reliability, volume reliability, and resiliency magnitudes of 0.631, 0.736, and 0.591, respectively. Finally, this study endorses to use the developed methodology for supply–demand management, and subsequent, reservoir operation in any global reservoir catchment-command regions with utmost accuracy. [ABSTRACT FROM AUTHOR]

The present study focuses on optimal sizing of an integrated renewable energy (IRE) system with battery bank to meet the load demand of a cluster of four zones in Karnataka state of India. Hourly load demand has been simulated using HOMER software based on three scenarios - high investment with low (HILR) rating appliances, medium investment with moderate (MIMR) rating appliances, and low investment with high (LIHR) rating appliances. Simulation results of optimal size, total net present cost (TNPC) and cost of energy (COE) have been obtained using genetic algorithm (GA) considering different scenarios with and without DSM strategy by keeping energy index at 1. Results of three scenarios with DSM strategy were compared with those without DSM strategy. Based on the comparison of the results, scenario of HILR with DSM strategy is found to have minimum TNPC and COE for zone 1 and zone 2, while scenario of MIMR with DSM for zone 3 and LIHR with DSM for zone 4 are found to have least TNPC and COE. Further, obtained results of TNPC of GA have been validated by comparing PSO technique. It has been observed that, results of GA and PSO were found to be almost same. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy interval optimization approach to solve the Environmental/ Economic Dispatch (EED) problem with uncertain parameters in the constraints and the objective functions. The objective functions considered are fuel cost and the gaseous emissions of the generating units. Two different types of fuel cost functions are considered in this study, namely, the conventional quadratic function and the augmented quadratic function to introduce more accurate modeling that incorporates the valve loading effects. The latter model presents non-differentiable and nonconvex regions that challenge most gradient-based optimization algorithms. In the proposed approach, objective functions are fuzzified and integrated to represent the fuzzy decision value. On the other hand, load uncertainties are modeled using fuzzy intervals. This fuzzy EED problem formulation provides a modeling flexibility, relaxation in constraints and allows the method to seek a practical solution. The obtained fuzzy multi-objective optimization problem is solved using non-dominated sorting genetic algorithm-II, known for its global searching capabilities, to get the best compromise among all the objectives. The performance of this solution is examined and applied to the standard IEEE 30-bus six-generator test system and the Indian power network of 82-bus by comparing its results with that of the existing methods. Different cases with different complexities have been considered in the study reported in this paper. [ABSTRACT FROM AUTHOR]

Over the years, renewable energy based power generation has proven to be a cost-effective solution in stand-alone applications in the regions where grid extension is difficult. The present study focused on the development of models for optimal sizing of integrated renewable energy (IRE) system to satisfy the energy needs in different load sectors of four different zones considered in Chamarajanagar district of Karnataka state in India. The objective of the study is to minimize the total cost of generation and cost of energy using genetic algorithm (GA) based approach. Considering optimization power factor (OPF) and expected energy not supplied (EENS), optimum system feasibility has been investigated. Based on the study, it has been found that IRES is able to provide a feasible solution between 1.0 and 0.8 OPF values. However, power deficit occurs at OPF values less than 0.8 and the proposed model becomes infeasible under such conditions. Customer interruption cost (CIC) and deficit energy (DE) for all zones were also computed to quantify the reliability of the systems. [ABSTRACT FROM AUTHOR]

Renewable energy, a viable alternative of the world, can overcome the energy crisis that hinders growth of today's economies. The present work includes sizing of hybrid energy system using various energy management strategies. The energy management strategies considered in the present work are cycle charging strategy, load following strategy and peak shaving strategy. The sizing of hybrid energy system for all the mentioned strategies are carried out using genetic algorithm, particle swarm optimization and biogeography based optimization techniques by keeping energy index ratio at 1. The model also incorporates net present cost, cost of energy, renewable fraction and emissions of carbon di-oxide from diesel generator. From the analysis, it can be concluded that the 10th combination of cycle charging strategy is most cost effective as compared to other dispatch strategies. Seven unelectrified villages of Dhauladevi block of Almora district in Uttarakhand, India, has been selected for the development of hybrid energy system, comprising of renewable energy resources available locally along with the addition of diesel generator, for meeting the energy deficit. The results show that cycle charging strategy is most cost effective compared to load following and peak shaving strategies, with a minimum cost of energy value of 5.64 ₹/kWh. [ABSTRACT FROM AUTHOR]

In this study, a support vector machine (SVM)-based ensemble model was developed for reliability forecasting. The hyperparameters of the SVM were selected by applying a genetic algorithm. Input variables of the SVM model were selected by maximizing the mean entropy value. The diverse members of the ensemble model were obtained by a k-means clustering algorithm, and one ensemble member was selected from each cluster by choosing the closest from the cluster center. The optimum cluster number was selected using the Davies-Bouldin index. The developed model was validated by a benchmark turbocharger data set. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted investigating a dumper operated at a coal mine in India. Time-to-failure historical data for the dumper were collected, and cumulative time to failure was calculated for reliability forecasting. Study results demonstrate that the developed model performs well with high accuracy (R² = 0.97) in the prediction of dumper failure, and a comparison with other methods demonstrates the superiority of the proposed ensemble SVM model. [ABSTRACT FROM AUTHOR]

The Indian housing finance market has been growing steadily at 17 percent in the recent years. At the same time, the loan default rate is also growing. In the present scenario, it is very important for the HFCs and the SCBs to minimize the loan defaults. One of the important strategies is to predict the likely defaulters so that such loans are either not issued or monitored closely after the issuance. This study attempts to build hybrid data mining models to predict the defaulters using data obtained from a major housing finance company. The hybrid predictive models were able to predict the default with high degree of accuracy. [ABSTRACT FROM AUTHOR]

Optimal planning of renewable energy generator (REG) units helps to meet future power demand with improved flexibility. Hence, this paper proposes a grid-oriented genetic algorithm (GOGA) based on a hybrid combination of a genetic algorithm (GA) and a solution using analytical power flow equations for optimal sizing and placement of REG units in a power system network. The objective of the GOGA is system loss minimization and flexibility improvement. The objective function expresses the system losses as a function of the power generated by different generators, using the Kron equation. A flexibility index (FI) is proposed to evaluate the improvement in the flexibility, based on the voltage deviations and system losses. A power flow run is performed after placement of REGs at various buses of the test system, and system losses are computed, which are considered as chromosome fitness values. The GOGA searches for the lowest value of the fitness function by changing the location of REG units. Crossover, mutation, and replacement operators are used by the GOGA to generate new chromosomes until the optimal solution is obtained in terms of size and location of REGs. A study is performed on a part of the practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India for the base year 2021 and the projected year 2031. Load forecasting for the 10-year time horizon is computed using a linear fit mathematical model. A cost–benefit analysis is performed, and it is established that the proposed GOGA provides a financially viable solution with improved flexibility. It is established that GOGA ensures high convergence speed and good solution accuracy. Further, the performance of the GOGA is superior compared to a conventional GA. [ABSTRACT FROM AUTHOR]