Hybrid Optimization Research Articles

Charging control of lithium‐ion battery and energy management system in electric vehicles

AbstractIn terms of electric vehicle architectures, the drivetrain offers unprecedented freedom, but it also creates new obstacles in terms of achieving all needs. The architecture of electric vehicles is simplified and adjustable at the component level because they don't have a combustion engine or fuel tank, only an electric motor and a battery. Implementing safe zones within electric vehicles (EVs) to accommodate battery packs necessitates significant adjustments to ensure the secure integration of the battery. A Battery EV, also known as a pure EV, solely relies on rechargeable battery packs as its source of energy, without any additional propulsion system. The Battery Management System (BMS) plays a significant role in maintaining the safety of electric vehicles by controlling the electronics of rechargeable batteries, whether they are individual cells or battery packs. The BMS plays crucial role in protecting both the user and the battery by monitoring and maintaining the cell's operation within safe limits. This research paper focuses on the control of solar‐powered charging for lithium‐ion batteries. An optimized FOPID controller is utilized to maximize power extraction from PV array and efficiently charge the battery. A hybrid optimization model is employed to optimize the gain parameters of the FOPID controller.

Intelligent optimal sitting and sizing of distributed resources in presence of renewable energy and fuel cells based on reliability and emission factors

As the integration of renewable energy sources—such as wind, solar, and fuel cells—into power systems increases, challenges related to system reliability and cost efficiency become paramount. This paper examines the reliability of a standard power network composed of diverse energy sources including wind turbines, solar panels, fuel cells, batteries, and diesel generators. This paper proposes a novel hybrid optimization approach utilizing a Grey Wolf Optimizer combined with another advanced algorithm to minimize the total cost of energy production, inclusive of emission penalties for CO2, NO2, and SO2. The proposed approach is tested on IEEE 57-bus and IEEE 118-bus systems, demonstrating significant improvements over existing algorithms. Quantitative results show that our method reduces the total cost by up to 18 % and enhances system reliability by reducing voltage deviations in various scenarios by 12 %. The paper also provides insights into the optimal sizing and placement of wind turbines, photovoltaics, diesel generators, and fuel cells under different emission constraints (15000, 25000, and 45000 kg), further emphasizing the flexibility and effectiveness of our method in addressing environmental and economic challenges in modern power systems.

A Fuzzy Wavelet Neural Network (FWNN) and Hybrid Optimization Machine Learning Technique for Traffic Flow Prediction

Traffic go with the flow forecasting is essential in urban planning and management, optimizing transportation structures and resource allocation. However, accurately predicting visitors glide is tough because of its inherent complexity, nonlinearity, and diverse uncertain factors. The trouble declaration underscores the issue in as it should be forecasting site visitors flow, mainly in urban environments characterized through dynamic and complex site visitor’s styles. In the existing paintings there are numerous traditional devices getting to know models used for visitors flow prediction, however those conventional strategies show off barriers in reaching excessive prediction accuracy. Therefore, the proposed work targets to put into effect hybrid optimization techniques for correct prediction in shipping machine. Here fuzzy wavelet neural community (FWNN) is used to address complicated nonlinear structures with uncertain conditions and hybrid optimization method called hybrid firefly and particle swarm optimization (HFO-PSO) which combines the exploration and exploitation talents of firefly and this fusion allows the version to capture intricate visitor’s styles efficiently and optimize the prediction technique, improving accuracy and efficiency. Moreover, the prediction performance of the proposed model is established and compared by means of the usage of distinct measures.

Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

ABSTRACT PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm.

Hybrid YSGOA and neural networks based software failure prediction in cloud systems

In the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing

Feasible synergy between hybrid solar PV and wind system for energy supply of a green building in Kota (India): A case study using iHOGA

This research article analyses the optimal feasible synergy options of renewable energy sources combined into a hybrid PV panel and wind energy power producing system to deliver green energy for the Renewable Energy Department building at Rajasthan Technical University Kota, India. The configuration of the modeled system is optimized to be estimated with improved hybrid optimization by genetic algorithm (iHOGA). This study generates operational conditions for this hybrid solar-wind energy system which simulates the energy usage process over one year. The optimal synergy configured between solar and wind hybrid systems provides peak-demand energy to the green office building with little surplus energy. The whole year’s production of energy was 6988 kWh and the energy used by the building was 6759 kWh. The energy loss estimated is 276 kWh/year. The CO2 mitigated by using the integrated renewable energy option is evaluated and observed at 5273.14 kg/year. The other pollutants like SO2 and NOx are also being mitigated by 9601.512 kg/year and NOx 32424.32 kg/year respectively.

A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models

A hybrid optimization filter for weather and wave numerical models is proposed and tested in this study. Parametrized Artificial Neural Networks are utilized in conjunction with Extended Kalman Filters to provide a novel postprocess strategy for 10 m wind speed, 2 m air temperature, and significant wave height simulations. The innovation of the developed model is the implementation of Feedforward Neural Networks and Radial Basis Function Neural Networks as estimators of an exogenous parameter that adjusts the covariance matrices of the Extended Kalman Filter process. This hybrid system is evaluated through a time window process leading to promising results, thus enabling a decrease in systematic errors alongside the restriction of the error variability and the corresponding forecast uncertainty. The obtained results showed that the average reduction of the systematic error exceeded 75%, while the corresponding nonsystematic part of that error decreased by 35%.

Optimal fuzzy P + Dµ controller for cancer chemotherapy

This paper aims to develop a robust, effective, and optimal controller for an automatic intravenous drug delivery system for the chemotherapy treatment of cancer patients. For this purpose, a novel hybrid control method, namely the Fuzzy P + Dµ controller, is designed by combining fractional calculus and fuzzy logic controller (FLC). Particle swarm optimization (PSO) algorithm is employed to tune the controller parameters optimally. To evaluate the performance of the proposed controller, a cancer patient model that considers cumulative drug toxicity, one of the most common side effects of chemotherapy, is utilized. Limits on toxicity and drug concentration for patient safety are incorporated into the controller design and optimization process. The graphical and numerical simulation results show that almost no cancerous cells are left at the end of the treatment period. The effectiveness of the controller is proven by the performance index (PI) obtained. Our optimal hybrid Fuzzy P + Dµ outclasses other control techniques in previous related studies with the best PI of 44.426 and the minimum final number of cancerous cells (FNCCs) of 5.084E-08. Moreover, in robustness tests perturbing intra-patient parameters and using different treatment protocols, the cumulative drug toxicity level is kept within safe limits, and almost no cancerous cells remain.

Strength Optimisation of Hybrid Bolted/Bonded Composite Joints Based on Finite Element Analysis.

A finite element analysis (FEA) was conducted to examine the behaviour of single-lap quasi-isotropic (QI) and cross-ply (CP) hybrid bolted/bonded (HBB) configurations subjected to tensile shear loading. Several critical design factors influencing the composite joint strength, failure conditions, and load-sharing mechanisms that would optimise the joining performance were assessed. The study of the stress concentration around the holes and along the adhesive layer highlights the fact that the HBB joints benefit from significantly lower stresses compared to only bolted joints, especially for CP configurations. The simulation results confirmed the redundancy of the middle bolt in a three-bolt HBB joint. The stiffness and plastic behaviour of the adhesive were found to be important factors that define the transition of the behaviour of the joint from a bolted type, where load sharing is predominant, to a bonded joint. The load-sharing potential, known as an indicator of the joint's performance, is improved by reducing the overlap length, using a low-stiffness, high-plasticity adhesive, and using thicker laminates in the QI layup configuration. Enhancing both the ratio of the edge distance to the hole diameter and washer size proves advantageous in reducing stresses within the adhesive layer, thereby improving the joint strength.

Techno-economic optimization and Strategic assessment of sustainable energy solutions for Powering remote communities

The purpose of this research is to identify the optimal configuration of a diesel generator with a hybrid renewable energy system that can sustainably meet the energy demands of a rural area. Some of the main challenges of using diesel generators to supply the required electric load in remote areas are the high operating cost, unmet electric load, carbon dioxide emissions, very high total net cost, and the quality of electricity. This study, which combined diesel generators with renewable resources, accurately analyzed eight different system outputs from the hybrid optimization of multiple energy resources software. The analysis considered factors such as site-specific conditions, fuel costs, and solar radiation levels. The software prefers a load following strategy over a cycle charging strategy. This load following strategy involves combining a diesel generator with renewable energy sources, specifically configured as a combination of photovoltaic cells, a diesel generator, a wind turbine, and a battery. This configuration, with a total net cost of approximately $180,065.43 and a levelized cost of energy of $0.32, is identified as the most effective configuration chosen. Operating cost, carbon dioxide emission, and total net present cost are reduced by 53.17 %, 60.46 %, and 30.02 %, respectively. It highlighted the combination of diesel generator with renewable energies and their role in promoting environmental sustainability. By providing 100 % of the load demand, it increased the quality of electricity such that the unmet electric load is reduced to zero.

Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding

Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.

Predictive Placement of IC Chips using ANN-GA Approach for Efficient Thermal Cooling

In this research, numerical modelling is used to explore the heat transfer through natural convection capabilities of nine aluminum integrated circuit chips that are installed on substrate board. The goal is to figure out where on the substrate board these IC chips would be best placed if they were arranged differently. The dimensionless parameter (λ) plays a very essential role, and by applying a hybrid technique consisting of ANN and GA. ANSYS Icepack calculates IC chip temperature distributions in 3D steady state numerical simulations. It has been shown that the form, dimensions, and IC chips' substrate board positioning affects their operating temperature. In comparison to the strategies that have been used in the past, hybrid optimization is the strategy that has shown to be the most reliable in properly predicting how the IC chips would be arranged on the substrate board. It has been observed that higher values of one of these parameters lead to a reduction in the maximum temperature surplus. A correlation has been established to illustrate this relationship as it increases. The most favorable simulation outcomes are utilized to drive a genetic algorithm (GA), which identifies the optimal configuration ensuring that the temperatures of the heat sources remain well below their specified maximum operating conditions, as outlined in the data sheets. The maximum temperature variation between the lowest and highest extreme configurations ranges between 4 - 8%. The smallest size IC chip, U2 with high heat dissipation rate attains the maximum temperature in the configuration, however, the temperature variation for the low powered IC chips U3, U4 and U7 are very small. Found good agreement of both the data with an error band of 10%, and thus confirms the accuracy of the network.

Hybrid GRU-CNN bilinear parameters initialization for quantum approximate optimization algorithm

The Quantum Approximate Optimization Algorithm (QAOA), a pivotal paradigm in the realm of variational quantum algorithms (VQAs), offers promising computational advantages for tackling combinatorial optimization problems. Well-defined initial circuit parameters, responsible for preparing a parameterized quantum state encoding the solution, play a key role in optimizing QAOA. However, classical optimization techniques encounter challenges in discerning optimal parameters that align with the optimal solution. In this work, we propose a hybrid optimization approach that integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and a bilinear strategy as an innovative alternative to conventional optimizers for predicting optimal parameters of QAOA circuits. GRU serves to stochastically initialize favorable parameters for depth-1 circuits, while CNN predicts initial parameters for depth-2 circuits based on the optimized parameters of depth-1 circuits. To assess the efficacy of our approach, we conducted a comparative analysis with traditional initialization methods using QAOA on Erdős-Rényi graph instances, revealing superior optimal approximation ratios. We employ the bilinear strategy to initialize QAOA circuit parameters at greater depths, with reference parameters obtained from GRU-CNN optimization. This approach allows us to forecast parameters for a depth-12 QAOA circuit, yielding a remarkable approximation ratio of 0.998 across 10 qubits, which surpasses that of the random initialization strategy and the PPN2 method at a depth of 10. The proposed hybrid GRU-CNN bilinear optimization method significantly improves the effectiveness and accuracy of parameters initialization, offering a promising iterative framework for QAOA that elevates its performance.

A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks

Abstract. Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning

Brain–computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model’s overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments

Remaining useful life prediction method of rolling bearings based on improved 3σ and DBO-HKELM

Abstract An improved 3σ method and dung beetle algorithm optimization hybrid kernel extreme learning machine-based (DBO-HKELM) approach for predicting the remaining useful life (RUL) of rolling bearings was suggested in order to increase prediction accuracy. Firstly, multi-dimensional degradation feature data is extracted from bearing vibration data. Considering the influence of noise signal on the prediction accuracy, an improved kernel principal component analysis method is proposed to reduce the noise of degraded features. Then, an improved 3σ method is proposed to determine the starting point of bearing degradation by combining bearing vibration signal data. Lastly, a DBO-HKELM life prediction model was put forth. The parameters of hybrid kernel extreme learning machine were optimized by dung beetle algorithm, and appropriate kernel parameters and regularization coefficient were selected. The feature set of degradation indicators is input into the trained model to output the bearing RUL prediction results starting from the determined degradation starting point. Multiple data sets were used to verify that the new RUL prediction method significantly improves the prediction accuracy.

Optimizing Production Planning and Inventory Management in Post-Pandemic Recovery Using A Multi-Period Hybrid Uncertain Optimization Model

During the post-COVID-19 pandemic recovery phase, decision-makers in the manufacturing and retail sectors are confronted with numerous uncertainties. These issues comprise various aspects of operations, including the acquisition of raw materials or components and planning production activities. Therefore, this research aimed to introduce an innovative dynamic hybrid optimization model that combined probabilistic and fuzzy techniques. The model would offer a solution for addressing the challenges posed by uncertain parameters, particularly in the context of post-pandemic scenarios for production planning and inventory management with multiple periods of observation. The model was designed to handle exceptional circumstances such as parameter uncertainties, augmented demand fluctuations, fuzzy variables, and probabilistic factors. The primary objective of the model was to maximize the expected total profit of the operational process. To achieve this aim, an uncertain programming algorithm based on the interior point method was used to compute the optimal decision for the problem at hand. Through the execution of simulations using randomly generated data, the proposed model was thoroughly evaluated and analyzed with six suppliers, three raw part types, three product types, and six periods. All six suppliers were selected to supply raw parts, however, not all suppliers were selected to supply particular raw part types. Furthermore, it was derived that the expectation of the maximum profit is 897261.40; this is the best expected profit generated by the optimization model, meaning that other decisions may result in a smaller expectation of the profit. The results of these simulations unequivocally showed the effectiveness of the decision-making model in providing optimal solutions, specifically in terms of raw material procurement and production planning strategies. Subsequently, this model could serve as a valuable tool for decision-makers operating within the manufacturing and retail industries.

Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm

Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm

Techno-economic analysis of a PV/WT/biomass off-grid hybrid power system for rural electrification in northern Morocco using HOMER

The increasing environmental concerns associated with fossil fuels have elevated the significance of sustainable energy sources such as solar, wind, and biomass. This study aims to design a hybrid renewable energy system capable of meeting the annual energy demand of residential areas in Zoumi's circle, estimated at 15545.13 kWh/day. Using HOMER Pro software, we analyze various combinations of renewable resources (solar, wind, biomass) and storage solutions (batteries) to assess their electricity generation, cost implications, and environmental impact. The study identifies an optimal hybrid configuration that includes solar, wind, biomass, batteries, and converters. This integrated system proves to be the most cost-effective option, achieving an energy cost of 0.125 $/kWh and a net present cost (NPC) of 8.29 $ million. It generates 11.14 GWh/year and achieves a 100 % renewable energy ratio, significantly reducing CO2 emissions by approximately 5900 tons/year compared to diesel generators.

A novel squirrel-cat optimization based optimal expansion planning for distribution system

AimIn recent years, renewable distributed generation (DG) has grown to deliver sustainable electricity with minimal environmental impact. However, renewable DG poses new provocation in the distribution system expansion planning problem (DSEP). To address those problems, this paper suggests a new mathematical model for distribution system expansion plans using a novel hybrid optimization strategy. MethodThe optimal expansion plan for the distribution system is achieved using the hybrid optimization model, named Squirrel Search Insisted Cat Swarm Optimization (SSI-CS) algorithm. The proposed hybrid optimization algorithm is developed with the incorporation of the characteristics features of Squirrel search optimization (SSA) algorithm and Cat Swarm Optimization (CSO) Algorithm to optimize the solar capacity, wind capacity and biomass capacity. This combination aims to strike a balance between global and local optimization, ultimately leading to better cost-effective results. The Distribution systems variables like DG type, size/capacity, location, real power, reactive power, and the solar and wind capacity during load demand uncertainty act as the input to the proposed hybrid optimization algorithm. The main objective of attaining minimal cost for the expansion plan of the distribution system is checked, and the cycle is repeated until obtaining the optimal solution (minimum cost). ResultThe experimental analysis using an IEEE-33 bus system with 5 system states is executed in MATLAB/Simulink. The suggested SSI-CS model attained a minimal operational cost of 7433.4 which better than GWO, SSA and CSO. ConclusionHence, the proposed SSI-CS shows promise as an efficient and effective approach for distribution system expansion planning.