Non-dominated Sorting Genetic Algorithm II Research Articles

Experimental evaluation and optimization of parameters affecting delamination, geometrical tolerance and surface roughness in ultrasonic drilling of 3D-Printed PLA thermoplastic

Ultrasonic drilling is a suitable process to enhance the generated surfaces by additive manufacturing. In this study, polylactic acid was selected as the workpiece. The examination parameters were thrust force, delamination, geometrical tolerance, chip adhesion, hole wall morphology and surface roughness. It was explained that the harmonic movement of drill bit in ultrasonic drilling reduced thrust force, delamination, circularity, cylindricality and surface roughness up to 14.5%, 3.7%, 44%, 38%, 20% respectively and removed chip adhesion. Furthermore, number of end-mill flutes was examined and observed that 4-flutes compared to 2-flutes induced reduction in thrust force, delamination, circularity, cylindricality and surface roughness up to 15.2%, 2%, 7.5%, 18.9%, 12.5% respectively. Besides, analysis of variance was established to determine the significant parameters. Finally, non-dominated sorting genetic algorithm-II technique was implemented in order to carry out multi-response optimization.

Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector's evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity –as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being –reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.

Modeling and optimizing linear projects using LSM and Non-dominated Sorting Genetic Algorithm (NSGA-II)

Linear projects are a special type of repetitive projects. Major disadvantages have been shown when using the critical path method (CPM) for linear project problems. So, to overcome these shortcomings linear scheduling method (LSM) was used to analyze linear projects. There are different types of activities in linear projects. However, existing mathematical models for LSM cannot represent all types of linear project activities. Therefore, this paper presents a mathematical model that can represent all types of activities using matrices. Moreover, the mathematical model is then used to create an optimization model that minimizes the project duration and cost using non-dominated sorting genetic algorithm II (NSGA-II). The two models can help project managers in scheduling real-life linear projects and find optimum linear project schedules. This paper introduces an optimization model for linear projects that utilizes matrices, offering a different approach in the field of linear project optimization.

Schedule-cost optimization in high-rise buildings considering uncertainty

PurposeThe paper aims to develop a useful tool that can reliably and accurately find the critical paths of high-rise buildings and provide optimal solutions considering the uncertainty based on Monte Carlo simulation (MCS) to enhance project implementation performance by assisting site workers and project managers in high-rise building engineering.Design/methodology/approachThis research proposes an approach integrating the improved nondominated sorting genetic algorithm II (NSGA-II) considering uncertainty and delay scenarios simulated by MCS with the technique for order preference by similarity to an ideal solution.FindingsThe results demonstrate that the proposed approach is capable of generating optimal solutions, which can improve the construction performance of high-rise buildings and guide the implementation management for shortening building engineering project schedule and cost under the delay conditions.Research limitations/implicationsIn this study, only the construction data of the two floors was focused due to the project at the construction stage, and future work can analyze the whole construction stage of the high-rise building to examine the performance of the approach, and the multi-objective optimization (MOO) only considered two factors as objectives, where more objectives, such as schedule, cost and quality, can be expanded in future.Practical implicationsThe approach proposed in this research can be successfully applied to the construction process of high-rise buildings, which can be a guidance basis for optimizing the performance of high-rise building construction.Originality/valueThe innovations and advantages derived from the proposed approach underline its capability to handle project construction scheduling optimization (CSO) problems with different performance objectives under uncertainty and delay conditions.

Dynamic analysis and optimization of a novel Dual-valve heave compensator for heavy lifting operations

Traditional Passive Heave Compensator (PHC) requires set parameters before operation according to the specific sea conditions, making it difficult to adapt to complex and variable working environments. This study aims to enhance the environmental adaptability of classical PHC by proposing a novel concept of Dual-valve Heave Compensator (DHC) based on the traditional PHC framework. By modulating the hydraulic and pneumatic throttle valve openings dynamically, the DHC system facilitates adjustments extensively in damping and stiffness, significantly enhancing compensation performances. As an enhancement over traditional models, this study incorporates a coupled dynamic model that accounts for critical influencing factors, including gas temperature and nonlinear friction. A linearized equation for the dynamic stiffness of DHC has been developed based on the small deviation linearization method, extensively analysing the effects of pneumatic parameters on the stiffness characteristics. Furthermore, a design framework employing Latin Hypercube Sampling (LHS) design, Radial Basis Function (RBF) surrogate model, and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been specifically developed to address the multi-objective optimization challenges presented by DHC systems. The case study indicates that the optimized DHC can achieve up to 86% compensation accuracy under a 250-ton load with harmonic excitation and offers adjustments flexibly for various operational conditions. Contrary to conventional wisdom, minimal valve openings significantly enhance compensation accuracy during some intervals of irregular wave excitation. These findings confirm the superior performance and potential of DHC relative to traditional PHCs under complex marine environments.

Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.

Microstructural and mechanical characterization of direct energy deposited 316L stainless steel developed using an evolutionary computational approach

Wire arc additive manufacturing (WAAM) is a technology that has revolutionized the fabrication of large metallic components in various industries. However, previous studies have predominantly relied on trial and error or statistical techniques to determine process parameters, neglecting the potential of evolutionary algorithms for optimizing. In the present work, detailed investigations were performed on the WAAM-based gas tungsten arc welding to predict and optimize the process parameters of the 316L austenitic stainless steel. The input process parameters viz., current, wire feed speed, travel speed, and wire feed direction play a significant role in determining the single bead geometry. The study investigated the influence of these parameters on bead characteristics (such as height to width ratio and percentage dilution) and surface roughness. Multiobjective optimization was carried out using both the response surface methodology-desirability approach (RSM-DA) and the non-dominated sorting genetic algorithm-II (NSGA-II). In the case of NSGA-II, the optimal parameters were determined through a fuzzy decision method and it was validated experimentally. The NSGA-II approach showed superior performance over the traditional RSM-DA model in predicting the responses. In addition, the thin-walled structure printed for the parameters suggested using RSM-DA and NSGA-II approach. In a comprehensive property-based comparative analysis, the NSGA-II optimized parameters demonstrated improved mechanical properties attributed to increased low-angle grain boundaries and kernel average misorientation values. This study highlights the effectiveness of evolutionary computation methods in achieving superior performance and mechanical characteristics in the fabrication of SS316L components.

Structural optimization of PVDF cellular resonators for energy-harvesting enhancement based on backpropagation neural network and NSGA-II algorithm

Metamaterials with honeycomb structures have garnered significant attention in energy-harvesting applications. In this study, we present three conventional cantilever resonators with honeycomb substrates featuring positive, zero, and negative Poisson’s ratios (PPR, ZPR, and NPR). Polyvinylidene fluoride consisting electrode layers is attached to the substrate surfaces. A process parameter optimization method of honeycomb structure design was proposed, in which finite element simulation for data generation, backpropagation neural network for data prediction, and nondominated sorting genetic algorithm II (NSGA-II) for data optimization. This method aims to determine the optimum geometric parameters of the honeycomb structures, which addresses general cantilever resonators, where the first structural vibration modes are typically of interest and have to match specific target eigenfrequencies for engineering applications. Finite element simulations show that the peak voltage of PPR, ZPR, and NPR resonators at 75 Hz is increased by 12 %, 22 %, and 32 %, respectively, due to optimization. The optimized structures are fabricated and measured to validate the numerical model. The performance of the resonators with cellular materials featuring a full characteristic range of Poisson’s ratios is then compared systematically to explore their energy-harvesting potential. The results revealed that the NPR structure exhibits superior performance for energy-harvesting. Moreover, stress distribution and displacement have also been highlighted in this paper.

Effect of ergonomic aspects on single‐ and multiproduct assembly‐line balancing problems

AbstractIncorporating ergonomic considerations into an assembly‐line balancing problem (ALBP) enhances productivity and minimizes ergonomic concerns. The assembly process, characterized by repetitive motions and handling numerous components, can lead to worker overload. Consequently, the inclusion of ergonomic aspects results in an appropriate distribution of assembly operations and relative workloads. This study investigates a multi‐objective ALBP aimed at minimizing the number of workstations, overall skill level required, and variance in workers' energy expenditure across workstations. To address the ALBP while considering the ergonomic aspects, this study proposes an approach based on the non‐dominated sorting genetic algorithm II (NSGA‐II) and multi‐objective simulated annealing (MOSA) using Pareto optimality. A comparative analysis of the NSGA‐II and MOSA is conducted in single‐ and multiproduct production scenarios, and a computational study involving various factors is performed to identify the dominant algorithm. The computational analysis indicates that the runtime performance of MOSA is 73.287% better than that of NSGA‐II; therefore, MOSA outperforms NSGA‐II. This study aims at applying scientific knowledge concerning manufacturing ergonomics to assist manufacturing industries in enhancing their productivity.

Non-dominated sorting artificial rabbit multi-objective sizing optimization for a conceptual powertrain of a 6 × 4 battery electric tractor truck

In order to tackle the increasingly severe energy crisis, improving the efficiency of the powertrain system in battery electric vehicles has become an effective approach. Based on a 6 × 4 battery electric tractor truck (BETT), the paper focuses on enhancing the dynamic and economic performance of vehicles through advancements in configuration, control strategies, and parameter design. Firstly, a novel multi-motor torque coupling powertrain system based on the parallel-axle transmission is proposed and modeled. Then, an instantaneous minimum energy consumption control strategy for the economic performance simulation and an instantaneous maximum acceleration control strategy for the dynamic performance simulation are designed. Furthermore, a conventional heuristic single-objective optimization algorithm, artificial rabbit optimization (ARO), is improved into a multi-objectives version, non-dominated sorting artificial rabbit multi-objective optimization (NSARMO), by combining the fast non-dominated sorting (FNS) and modifying the calculation method of the energy factor. Finally, an optimization framework consisting of a control layer, model layer, and optimization layer is established and applied to the parameter optimization of the proposed powertrain system. Simulation results demonstrate that the optimization results of the proposed method are superior to those optimized by commonly used non-dominated sorting genetic algorithm-II (NSGA-II) and NSARMO without the modified energy factor, achieving a better balance between dynamic performance and economic performance. Moreover, the proposed method exhibits demonstrates 40.67% and 24.67% improvement in its ability to search for optimal Pareto solutions compared to NSARMO (-)(- means the calculation method of energy factor is not modified) and NSGA-II. At last, a hardware-in-the-loop (HIL) experiment bench is established, and the experiment results indicate that a scheme optimized by the proposed NSARMO exhibits only an approximate performance in energy consumption in the HIL environment compared to the simulation environment, which signifies the effectiveness of control strategy equipped with the optimization results optimized by the proposed method.

Intelligent Energy System and Power Metering Optimization Based on the Energy Plan Model

With the national carbon neutrality and carbon peaking policies proposed one after another, this paper proposes intelligent Energy system design and power metering based on the Energy Plan model. In this paper, the Energy PLAN model is proposed as the basic model for energy development planning during the 14th Five-Year Plan period, and the simulation calculation is carried out for the energy system in 2020. The research shows that there is a large gap between the peak and valley of power load, the peak time of power supply and demand does not match, and the response-ability of the energy supply department is poor. Thus, the problems to be solved in the development of energy are determined. In addition, the accuracy of the model is further verified by comparing the actual data with the simulation results of Energy PLAN, and the direction of energy structure reform is determined. By coupling the NSGA-II (Non-dominated Sorting Genetic Algorithms-II) algorithm with the development model, a dual-objective multi-time scale scheduling model is established, which aims at the minimum operating cost and the best energy and environmental benefit coefficient. Secondly, the proposed model is solved using the NSGA-II algorithm. In order to ensure the diversity of solutions and promote the Pareto front approach to the ideal Pareto front, the fuzzy dominance method is used to perform a fast non-dominated sorting of the population, and the maximum satisfaction method is used to select the Pareto optimal compromise solution. Example analysis shows that when the power demand level is L*-L*-L*, the power input under “H-M”, “M-L” and “H-L” scenarios is 106.02 × 103, 102.73 × 103 and 94.2 × 103 GWh, respectively. However, when the power demand level is H*-L*-L*, the power input is always 142.64 × 103 GWh. With the decrease in the transmission rate, the measurement data obtained by the user’s electric acquisition platform becomes less and less, and the performance of state estimation becomes worse. When λ=50%, the proposed model still maintains a high estimation accuracy.

Searching optimal process parameters for desired layer geometry in wire-laser directed energy deposition based on machine learning

ABSTRACT The quantitative prediction of more process parameter variables for fewer layer geometry variables is challenging in wire-laser DED. This study’s novelty is combining machine learning models with a non-dominated sorting genetic algorithm-II (NSGA-II) to predict process parameters for desired layer geometries accurately. Thirty single-layer deposition experiments are conducted to obtain response data of layer geometries to process parameters. Two support vector regression (SVR) models are trained by these data to predict the layer height and width, respectively, and the mean absolute percentage errors (MAPEs) of these models are 4.16% and 1.76%. A reverse system, consisting of both SVR models and the NSGA-II algorithm, is designed to search the optimal process parameters for the desired layer geometries. The maximum MAPE between the actual layer geometry deposited by the predicted process parameters and the desired layer geometry is less than 5.5%, providing solid confirmation of this methodology’s reliability.

Prediction and design optimization of mechanical properties for rubber fertilizer hose reinforced with helically wrapped nylon

Predicting and optimizing the mechanical performance of the helically wound nylon-reinforced rubber fertilizer hose (HWNR hose) is crucial for enhancing the performance of hose pumps. This study aims to enhance the service life of HWNR hoses and the efficiency of liquid fertilizer transport. First, a finite element simulation model and a mathematical model were established to analyze the influence of fiber layer arrangement on the maximum shear strain on the coaxial surface (MSS) and the reaction force on the extrusion roller (RF). For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). Results showed CPO-GRNN's superiority in handling complex nonlinear problems. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for optimization design. Compared to the original HWNR hose with an MSS of 0.906 and an RF of 30,376N, the optimized design reduced the MSS by 7.99% and increased the RF by 2.46%, significantly enhancing their service life and liquid fertilizer transport capacity. However, further research on fatigue damage is needed.

Deep Heterogeneous AutoML Trend Prediction Model for Algorithmic Trading in the USD/COP Colombian FX Market Through Limit Order Book (LOB)

This study presents a novel and competitive approach for algorithmic trading in the Colombian US dollar inter-bank market (SET-FX). At the core of this strategy is an advanced predictive model, developed using the Tree-based Pipeline Optimization Tool (TPOT). TPOT, an automated machine learning platform based on strongly-typed genetic programming, incorporates the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This multi-objective evolutionary algorithm is instrumental in identifying machine learning models that strike an optimal balance between high accuracy and low complexity, thereby advancing the field of predictive modeling in financial markets.

Hierarchical thermal modeling and surrogate-model-based design optimization framework for cold plates used in battery thermal management systems

The continued advancement of battery-powered electric vehicles (EVs) towards higher energy and power densities poses significant thermal challenges for batteries. This work proposes a generalized design optimization framework for battery thermal management systems (BTMS), responding to the need for EVs with enhanced battery thermal performance, safety, and lifetime. This framework combines hierarchical thermal modeling and surrogate-model-based design optimization, explicitly tailored for liquid-cooled cold plates commonly used in EV BTMS. The hierarchical thermal modeling component decouples the battery cell, battery module, and cold plate models to reduce computational modeling costs while preserving the relative thermal performance of different cold plate designs. The design optimization component leverages our pioneer deep encoder–decoder hierarchical (DeepEDH) convolutional neural network surrogate modeling methodology to predict the cold plate’s full pressure, velocity, and temperature fields. Computationally efficient DeepEDH neural networks replace costly transient thermal simulations of cold plates and compute the objectives for evolutionary optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This work’s novel modeling and optimization framework produces optimal cold plate designs that consider battery-module-specific design features, including distributed battery cell heat generation, packaging materials, and heat-spreading characteristics. The proposed framework is evaluated through a cold plate design for modular BTMS, thoroughly assessing the impact of optimization objectives, flow path constraints, modeling assumptions, and design variable choices. The results demonstrate the effectiveness of the combined hierarchical thermal modeling and design optimization framework, with the optimized cold plates achieving reductions of 6.26K, 6.29K (22.7%), and 16.16Pa (18.7%) for maximum temperature, maximum temperature difference, and pressure loss, respectively. Moreover, compared to other methodologies that apply direct optimization without hierarchical and surrogate models, our approach significantly reduces the computational cost — from approximately 4800h to 13.5h. Our generalized multi-objective optimization framework is an effective and efficient design tool that can be leveraged to advance thermal management innovations for next-generation battery systems.

Numerical two-dimensional optimization of a cylindrical fuselage for bioinspired unmanned aerial vehicle based on non-dominated sorting genetic algorithm-II

To achieve an aerodynamically efficient design of fuselage for bioinspired unmanned aerial vehicles (UAV), the development of a systematic method is of paramount need. This work presents a multiobjective optimization study for shape parameterization of a 2D fuselage of foldable flapping wing UAV in low Reynolds number (2e05) flight conditions. The novelty of the research work lies in integrating S1223 airfoil characteristics for predicting the efficient shape design of the fuselage. This paper offers four sections; (i) the validation of the ANSYS Fluent model with experiment, (ii) the exploration of design variables using Design of Experiment (Sparse Grid Initialization), (iii) the implementation of response surface method (Genetic Aggregation) to know about dimensional sensitivity among independent and dependent variables and (iv) the application of multiobjective optimization method i.e. NSGA II to optimize the drag and lift coefficient. To identify the superiority, a comparative study between the original and optimized fuselage is presented considering many parameters like pressure contours, velocity contours, pressure coefficients, and streamlines representations. It is evident from various results that the 2D optimum shape significantly minimizes the drag coefficient and increases the lift coefficient. This work also makes room for 3D shape optimization, which helps in prototype fabrication for real-time flight conditions.

Drone-Based Instant Delivery Hub-and-Spoke Network Optimization

Drone-based transportation is emerging as a novel mode in city logistics, featuring first-mile pickup and last-mile instant delivery using drones and truck transshipment. A fundamental challenge involves coordinating merchants, drones, transshipment hubs, trucks, and consumer communities through the hub-and-spoke network (HSN). This study formulated the optimization problem for HSN to minimize logistics costs and loss of orders constrained by service time limits. The ε-constraint model, two evolutionary algorithms based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) using permutation (EAp) and rand key-based (EAr) encoding/decoding schemes were devised to solve the bi-objective mathematical program. Three groups of twelve experiments were conducted using ideal datasets and datasets generated from Shenzhen city to validate the models and algorithms. Relaxing the logistics objective by 10% and subsequently minimizing the loss of orders can significantly reduce average unmet orders by 24.61%; when spokes were beyond 20, the ε-constraint model failed to achieve solutions within an acceptable time. While EAp and EAr demonstrated competence, EAr proved to be more competitive in computation time, hypervolume, spacing metric, and the number of non-dominated solutions on the Pareto fronts. Key parameters influencing the HSN solutions include drone and truck speeds, acceptable delivery times, and the processing and waiting time at hubs.

Customised product design optimisation considering module synergy effects and expert preferences

ABSTRACT Fierce marketing competition and varied customer requirements on products make a lot of companies transfer to the customisation strategy. However, customers may be overwhelmed by the vast assortment of products and options. This paper proposes a methodology aimed at optimising the range of customised product varieties considering synergy effects of modules and preferences of experts. The methodology outlines key steps required for a successful customised product configuration. Firstly, a questionnaire based on the Kano model is designed to identify customer preferences for product module customisation. The potential customers can be segmented based on the distance among their multidimensional preferences. Secondly, within each segmented market, the customer satisfaction function that considers module synergies is established using the quantified Kano model and coefficient matrix. Thirdly, the cost function that considers experts' preferences is established based on the prospect theory. Finally, a bi-objective optimisation model is formulated to maximise customer satisfaction and minimise cost under technical constraints. Pareto solutions are obtained by solving the model with the non-dominated sorting genetic algorithm II (NSGA-II). Modules and attributes available to customers can be determined by the solutions. An illustrative example of modular laptop computers confirms the methodology's effectiveness in optimising product configuration.

NSGA-II based short-term building energy management using optimal LSTM-MLP forecasts

To conduct analysis on the field of electricity management in buildings is crucial to contribute to the clean energy promotion, energy efficiency, and resilience against climate change. This manuscript proposes a methodology for modeling a predictive and calibrated energy management system (EMS) using a hybrid methodology that combines long short-term memory and multilayer perceptron models (LSTM-MLP) optimized by non-dominated sorting genetic algorithm II (NSGA-II). The proposed approach utilizes global forecast system (GFS) data to anticipate energy consumption fluctuations and optimize the use of distributed energy sources, such as photovoltaic (PV) production, based on knowledge of the electricity prices in the free market one day ahead. This energy trade-off in the building is conducted with NSGA-II, guaranteeing the exploration and the exploitation of energy in the building while minimizing energy costs and energy wastes. The research carried out demonstrates the effectiveness of the hybrid LSTM-MLP model and the advantages of NSGA-II in hyperparameter tuning and in energy balance for sustainable building practices. The methodology conducted is tested in an existing building, the Industrial Engineering School located on the Campus Lagoas-Marcosende of the Universidade de Vigo, Spain.

Investigation of Lithium-Ion Battery Negative Pulsed Charging Strategy Using Non-Dominated Sorting Genetic Algorithm II

To address the critical issue of polarization during lithium-ion battery charging and its adverse impact on battery capacity and lifespan, this research employs a comprehensive strategy that considers the charging duration, efficiency, and temperature increase. Central to this approach is the proposal of a novel negative pulsed charging technique optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This study initiates the creation of an intricate electrothermal coupling model, which simulates variations in internal battery parameters throughout the charging cycle. Subsequently, NSGA-II is implemented in MATLAB to fine-tune pulsed charging and discharging profiles, generating a Pareto front showcasing an array of optimal solutions tailored to a spectrum of goals. Leveraging the capabilities of the COMSOL Multiphysics software 6.2 platform, a high-fidelity simulation environment for lithium-ion battery charging is established that incorporates three charging strategies: constant-current (CC) charging, a multi-stage constant-current (MS-CC) charging protocol, and a pulsed-current (PC) charging strategy. This setup works as a powerful instrument for assessing the individual effects of these strategies on battery characteristics. The simulation results strongly support the superiority of the proposed pulsed-current charging strategy, which excels in increasing the battery temperature and amplifying battery charge capacity. This dual achievement not only bolsters charging efficiency significantly but also underscores the strategy’s potential to augment both the practical utility and long-term viability of lithium-ion batteries, thereby contributing to the advancement of sustainable energy storage solutions.