Algorithm Optimization Technique Research Articles

Research on the construction of intelligent-aided design platform for green buildings

While socio-economic development and technological advancement bring great convenience to our life, energy consumption and environmental pollution have also become the core issues affecting human survival and development. This study focuses on the development of an intelligent design platform for green buildings, aimed at promoting sustainability through low energy consumption and reduced environmental impact. The study of existing national standards and requirements for the intelligent design of green buildings, combined with the unique characteristics of green buildings and user needs, has led to the development of an optimized auxiliary design platform system. By incorporating algorithm optimization and modeling techniques, this platform enhances the design process. Experimental evaluations of the system reveal that the intelligent auxiliary design offers more accurate predictions, thereby improving user service and experience. Additionally, the system contributes to energy efficiency and environmental sustainability by reducing the building’s ecological footprint. This research highlights the potential of intelligent systems to facilitate more efficient and environmentally friendly green building designs.

Performance optimisation of axial flux permanent magnet brushless DC motor for electric vehicle application with the genetic algorithm (GA) approach

This paper presents the multi-objective performance optimisation of an axial flux permanent magnet brushless dc motor for electric vehicle application. An axial flux brushless motor is designed for direct drive application in electric two-wheelers. It is quite important to improve the efficiency and weight simultaneously, hence multi-objective optimisation is performed. The efficiency and weight of the motor are two opposite performance parameters hence it is a challenging task to improve both simultaneously. The main contribution of the present work is to propose the best combination of design variables obtained using the genetic algorithm optimisation technique. The proposed technique is validated with finite element analysis. The proposed optimisation technique is effective in performance improvement of axial flux brushless dc motors and practically implementable. The novelty of the proposed work is performance improvement without increasing manufacturing complexity – cost and adopting new materials. The proposed optimisation technique can be useful for multi-objective performance improvement of any nonlinear design involving various design variables.

Impacts of electric vehicle charging stations and DGs on RDS for improving voltage stability using honey badger algorithm

The intelligent computational technique used in this research handles the multi-objective voltage stability optimization (MOVSO) problem in radial distribution systems (RDS). The objectives of the proposed research are to minimize network loss, lower the average voltage deviation index (AVDI), and improve the voltage stability index (VSI) of RDS by taking into account the recently created distributed generators (DGs) and electric vehicle charging stations (EVCSs). To address the MOVSO problem, a novel and innovative honey badger algorithm (HBA) optimization technique is put forth. The two stages of HBA, known as the "digging" and "honey" phases, are responsible for effectively identifying the ideal position and appropriate quantity of EVCSs and DGs. The standard IEEE 33 node test system with different case studies is considered to validate the performance of HBA. The simulation results of improved voltage profile, minimized power loss, AVDI and improved VSI are tabulated. The proposed HBA fine-tunes the ideal position and size of the EVCSs to significantly enhance RDS performance under higher loading circumstances. To demonstrate the efficacy and originality of the suggested HBA, the numerical results are contrasted with those of earlier soft computing techniques.

Microstructural material design of pearlitic steel lamella for desired mechanical properties

Designing materials for a targeted mechanical property is a complex problem due to its structure and property relationship nature, traditionally relying on experimental methods. Computational approaches offer a promising alternative, but they are often computationally intensive. This limits their applicability for solving the goal-based structure design problem. This study focuses on the design of microstructure to achieve specific mechanical properties by combining generative adversarial networks, deep neural networks, and genetic algorithm optimization techniques. In this work to demonstrate the effectiveness of the proposed approach we select a widely used pearlitic steel lamella microstructure where the phase fraction of ferrite and cementite and the lamella distribution significantly influence the strength and ductility. A method for designing the pearlite microstructure features for the targeted mechanical properties, such as yield strength (σy), ultimate strength (σut), and fracture strain (εf) is developed by applying inverse microstructure design techniques to achieve the targeted mechanical properties, specifically in fully pearlitic steel materials. The findings hold great promise, particularly when coupled with additive manufacturing, in offering an efficient and versatile framework for structural materials design for targeted material applications.

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Optimized physics-informed neural network for analyzing the radiative-convective thermal performance of an inclined wavy porous fin

The significance of radiation and inclination on the temperature dispersion of the wavy porous fin has been addressed in the present study. Also, the influence of convection and internal heat generation on the thermal dissipation of the inclined wavy porous fin (IWPF) is examined. The pertinent temperature expression of the fin is represented using basic laws, and this equation is reduced to a dimensionless form via dimensionless variables. Additionally, a mix-encoding Genetic algorithm and Particle swarm optimization technique is shown to optimize the network hyperparameters. This resolves the issue of arbitrarily identifying the Physics informed neural networks (PINN's) ideal network and successfully limits local optimization during the training phase. Further, the equation is also resolved numerically using Runge-Kutta Fehlberg's fourth-fifth (RKF-45) scheme, and the solutions are subsequently used to verify the PINN model's applicability. The temperature results estimated by PINN and their associated RKF-45 values correlate excellently, which indicates the accuracy of the applied PINN model. The obtained findings denote that reduced measures of convective-conductive variables stimulate the IWPF's thermal distribution. An inclination angle of the fin has a significant impact on the thermal variation of the IWPF.

Lightweight aircraft design: Integrating biomechanics and algorithm optimization for drone construction using paper materials

Lightweight aircraft design emphasizes materials like composites, alloys, and advanced polymers to reduce weight while ensuring structural integrity. Streamlined aerodynamics, efficient propulsion systems, and optimized component layout further enhance performance. The purpose of this research is to develop an innovative aircraft design model for drones, integrating algorithm optimization techniques and lightweight materials to enhance the performance and efficiency of drones with the utilization of open-source software. We apply this structure to the construction of drones with fixed-wing (FW) and vertical take-off and landing (VTOL) configurations. The aircraft’s body is constructed using lightweight materials such as glass fiber fabric (Gff) and extruded polystyrene foam (XPS), employing a vacuum-assisted wet layup technique for fabrication. Multi-objective Co-evolving Ant Colony Optimization (MC-ACO) was used to improve the construction of drones with VTOL and FW capabilities. This strategy enabled it to be easier to achieve the trade-offs required for a generalist drone design, such as range, payload capabilities, and swarm operations, which were then evaluated against commercially available software to evaluate the effectiveness. Incorporating biomechanics into the design method permits higher consideration of user interaction with drones. The findings indicate that low-fidelity architecture is a suitable starting point for prototyping under limited time frames. The paper concludes with a discussion of the technical constraints of employing free software, as well as some practical concerns for flight testing drones with hybrid configurations.

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

Prediction model for hardness and tensile strength of graphene reinforced AZ 61 alloy based composite using Metaheuristic Algorithm

It is possible to produce improved material performance through the utilization of computational intelligence approaches such as genetic algorithms optimization technique which are discussed in this paper. The optimization of processes and the development of models that are driven by data are the key uses of these technologies. This article offers a comprehensive introduction of the topic of materials and discusses the ways in which computational intelligence techniques might be utilized to develop new materials. The present study envisages the development of data driven model that enables to derive desirable properties of the said composite; so, in order to secure the optimized subset of requirements (process parameters), a metaheuristic optimization tool is employed. We invoke the use of the Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ61 graphene nanoplate (GNP) composite.

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>20\\%$$\\end{document}) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document} g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88\\%$$\\end{document} more time than the latter.

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

Thermal performance analysis and optimization of a heat pipe-based electronic thermal management system using experimental data-driven neuro-genetic technique

Heat pipes are highly efficient heat transfer devices and are used widely to dissipate heat generated by electronic components, thereby maintaining their operating temperatures and preventing overheating. The present study deals with the thermal performance prediction of a cylindrical heat pipe (length 380 mm) based electronic thermal management system using steady-state experimental investigation. The experiments are conducted under varying operational conditions, including heat inputs (10–50 W), condenser cooling water inlet temperatures (288.15, 293.15, and 298.15 K), and flow rates (10, 25, and 40 LPH). The study shows that the evaporator temperature of the heat pipe consistently rises with the increase in heat inputs for all tested conditions. The lowest evaporator temperature (311.42 K) is noticed for the cooling water inlet temperature of 288.15 K across all heat inputs. Notably, at 10 LPH, the heat pipe supplied with cooling water at 288.15 K exhibits reductions of up to 1.47 % and 2.29 % compared to 293.15 K and 298.15 K, respectively. Similar trends are also observed at 25 LPH and 40 LPH. The heat pipe’s thermal resistance is lowest (0.95 K/W) at a cooling water inlet temperature of 298.15 K. At 10 LPH, there are reductions of 10.19 % and 5.62 % compared to 288.15 K and 293.15 K, and at 40 LPH, reductions are 15.66 % and 7.89 %. The heat pipe’s evaporator heat transfer coefficient also plays a significant role and is maximum for the cooling water inlet temperature of 288.15 K at a flow rate of 10 LPH. To understand the complex behavior of the heat pipe and for better prediction of its thermal performance, a neuro-genetic (experimental data-driven artificial neural network and genetic algorithm) optimization technique is considered in the study. This predicts the optimal combination of operating parameters (heat input, cooling water inlet temperature, and flow rate) to minimize the heat pipe’s evaporator wall temperature which is found to be 312.54 K at a heat input of 10 W, flow rate of 10 LPH, and cooling water inlet temperature of 289.14 K. These input combinations are further validated through the experiments and the error in the heat pipe’s evaporator temperature is found to be 0.66 % only. Overall, this neuro-genetic technique underscores the importance of optimizing operational parameters for enhanced heat pipe performance and offers valuable insights for electronic cooling systems.

Innovative protection schemes through hardware-in-the-loop dynamic testing.

In microgrid environments, the behaviour of Distributed Generation (DG) during fault conditions varies significantly based on DG types and penetration levels. Conventional Overcurrent Relays (OCRs) with standard time-current characteristics may exhibit limitations during excessive fault scenarios, leading to OCR operating delays and mis-coordination within the microgrid. This study proposes a novel constraint on the maximum Current Multiplier Setting (CMS) and utilizes Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) techniques to optimize the Time Multiplier Setting (TMS). Comparative dynamic analysis through real-time validation shows that the non-standard OCR approach outperforms the standard IEC scheme across all grid operation modes with different type, size and location of DGs. For instance, under F1 conditions, the tripping time of OCR1 was reduced from 0.0226 seconds (IEC) to 0.000981 seconds (non-standard). HIL results further affirm the efficacy of the proposed scheme. The optimization process, implemented in MATLAB and validated using ATP/EMTP simulations and SIPROTEC 7SJ62 relays, demonstrates enhanced microgrid protection coordination, improving system reliability and performance.

A two-stage optimization model for relief logistics and distribution to disaster survivors under two-fold uncertainty

Disasters are unforeseen occurrences requiring extensive transport deployment to support and relieve victims. Sometimes, this transportation is not feasible directly from some supply points to some destination points. Due to this tragedy, it is unclear precisely what is available at supply points, what is needed at destinations, how much transportation capacity there is, and what the routes are like. In this study, we investigate a two-stage multi-item fixed charge four-dimensional transportation problem using the concept of big data theory under the two-fold uncertainties. Here, the model’s parameters such as unit transportation costs, availabilities of items at the suppliers, fixed charges, capacities of conveyances, and demands of the items at the retailers are considered type-2 zigzag uncertain variables. Using big data theory and based on uncertain programming theory, two novel uncertain models are developed such as chance-constrained programming and expected value programming model. These two uncertain models transformed into the deterministic form via uncertainty inverse distribution theory. A critical value based reduction method with three categories (i.e., expected value, pessimistic value, and optimistic value) is applied to reduce the type-2 zigzag uncertain variable to the type-1 zigzag uncertain variable. The genetic algorithm and particle swarm optimization techniques have been proposed to find the optimal solution for the two deterministic models. The efficiency of our proposed approach is demonstrated with a real-life numerical example.

Design of electrostatic lenses through genetic algorithm and particle swarm optimisation methods integrated with differential algebra

Genetic algorithm (GA) and particle swarm optimisation (PSO) techniques have been integrated with the differential algebra (DA) method in charged particle optics to optimise an Einzel lens. The DA method is a robust and efficient tool for the calculation of aberration coefficients of electrostatic lenses, which makes use of nonstandard analysis for ray tracing a particle as it is subjected to the field generated by a lens. In this study, initial populations of lenses with random geometrical configurations are generated. These initial populations are then subjected to GA and PSO algorithms to alter the geometry of each lens for a set number of iterations. The lens performance is evaluated by calculating the spot size using the aberrations coefficients up to third-order generated by the DA method. Moreover, a focusing column comprising two lenses and a Wien filter was optimised using GA method.

Algorithmic optimization of quantum optical storage in solids

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth. Published by the American Physical Society 2024

An Innovative Approach for Mission Sharing and Route Planning of Swarm Unmanned Aerial Vehicles in Disaster Management

Fast and effective response in disaster situations is critical for the success of rescue operations. In this context, swarm Unmanned Aerial Vehicles (UAVs) play an important role in disaster response by rapidly scanning large areas and performing situation assessments. In this paper, we propose an innovative method for task allocation and route planning for swarm UAVs. By combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO) techniques, this method aims to ensure the most efficient movement of UAVs. First, clusters are created using GA to determine the regions of the disaster area that need to be scanned. At this stage, factors such as the capacities of the UAVs, their flight times, and the breadth of their mission areas are taken into account. Each UAV is optimized to scan a specific area assigned to it. Once the clusters are formed, the routes of the UAVs within each cluster are determined by the Ant Colony Algorithm (ACA). The route planning is tested both on Google Maps and in a simulation environment. Google Maps is used to evaluate the accuracy and feasibility of route planning based on real-world conditions, while the simulation environment provides the opportunity to test the behavior of the UAVs and the effectiveness of the routes in a virtual setting. With real-time data integration, the UAVs' route planning can be updated instantly and quickly adapted to emergency situations.

A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for Computer Vision.

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain.

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

Experimental Investigations and Optimization of Surface Roughness Using Response Surface Methodology Coupled with Genetic Algorithm and Particle Swarm Optimization Techniques in Grinding of Inconel 718

Experimental Investigations and Optimization of Surface Roughness Using Response Surface Methodology Coupled with Genetic Algorithm and Particle Swarm Optimization Techniques in Grinding of Inconel 718