Intelligent Optimization System Research Articles

Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence

Intelligent design technology for shear wall structures has great potential for enhancing design efficiency and addressing the challenges of tedious and repetitive design tasks. Recently, there has been a surge in the development of this technology. However, existing deep learning-based design methods for shear wall structures suffer from poor quality and usability issues. To address these challenges, this study proposes an intelligent design and optimization system for shear wall structures based on large language models (LLMs) and generative artificial intelligence (AI). The system employs an LLM as the core controller, which interacts with engineers to interpret their language descriptions and translate them into executable computer code. Subsequently, the system utilizes the corresponding structural generation and optimization methods to accomplish intelligent design tasks automatically. Furthermore, the system incorporates such critical factors as the empirical rules, mechanical performance, and material consumption into the structural optimization process. A unique three-level, two-stage optimization method is constructed based on topology, pattern, and size to enhance the overall design quality. Being able to complete the entire workflow of architectural drawing processing, structural scheme generation, and analysis model establishment, the proposed system enables automated and efficient design of shear wall structures. Through the analysis and validation of multiple cases, it was demonstrated that this system can significantly speed up the design by approximately 30 times compared to that of traditional methods whilst ensuring the safety and cost-effectiveness of the design schemes. Consequently, this study provides valuable insights for the advancement of automated structural design undertakings.

A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

The application of optimization theory and the algorithms that are generated from it has increased along with science and technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial optimization issues. Swarm intelligence optimization algorithms have been successful in machine learning, process control, and engineering prediction throughout the years and have been shown to be efficient in handling combinatorial optimization issues. An intelligent optimization system called the chicken swarm optimization algorithm (CSO) mimics the organic behavior of flocks of chickens. In the benchmark problem's optimization process as the objective function, it outperforms several popular intelligent optimization methods like PSO. The concept and advancement of the flock optimization algorithm, the comparison with other meta-heuristic algorithms, and the development trend are reviewed in order to further enhance the search performance of the algorithm and quicken the research and application process of the algorithm. The fundamental algorithm model is first described, and the enhanced chicken swarm optimization algorithm based on algorithm parameters, chaos and quantum optimization, learning strategy, and population diversity is then categorized and summarized using both domestic and international literature. The use of group optimization algorithms in the areas of feature extraction, image processing, robotic engineering, wireless sensor networks, and power. Second, it is evaluated in terms of benefits, drawbacks, and application in comparison to other meta-heuristic algorithms. Finally, the direction of flock optimization algorithm research and development is anticipated.

"Medical Support in the Selectively Integrated Returns Intelligent Optimization System – SEIRIOS"

"Medical Support in the Selectively Integrated Returns Intelligent Optimization System – SEIRIOS"

Performance evaluation of six mesh router replacement methods for wireless mesh networks: A comparison study for small and middle scale networks considering two islands distribution of mesh clients

In this paper, we present a hybrid intelligent simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs) called WMN-PSOHCDGA. We implemented six mesh router replacement methods: CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM and consider Two Islands distribution of mesh clients. We carry out a comparison study of these router replacement methods for small and middle scale WMNs. We assessed the performance by computer simulations. The simulation results show that six methods have a good performance for connectivity and coverage metrics, for both small and middle scale WMNs. However, they have different behavior for load balancing. For small scale WMNs, the load balancing of LDIWM, RIWM and FC-RDVM is better than CM, LDVM and RDVM. While, comparing LDIWM, RIWM and FC-RDVM, the LDIWM has better load balancing. We found that the load balancing for small scale WMNs is not good, because there is a concentration of mesh routers in some areas. For middle scale WMNs, the CM, LDIWM, LDVM and RDVM have not a good load balancing. While, the RIWM and FC-RDVM have better performance. Comparing RIWM and FC-RDVM, we found that the load balancing of FC-RDVM is better than RIWM.

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

An intelligent body frame structure modeling and optimization system based on conceptual design

The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.

Efficient energy transport in constant-voltage triboelectric nanogenerator-based power units

The energy transport in TENG-based power units is investigated and a real-time intelligent energy optimization system (RIEOS) is built to achieve efficient energy transport from a system integration optimization perspective.

Computer vision-based intelligent elevator information system for efficient demand-based operation and optimization

Occupancy and behavior in buildings are considered to be key factors influencing energy consumption. Residential areas contribute substantially to energy consumption in urban environments and have more regular users. Developing methods that can monitor building space utilization patterns and meet occupant comfort requirements is essential for urban energy efficiency. In this work, we propose an innovative computer vision-based Intelligent Elevator Information System (IEIS), which is based on a pipeline framework that sequentially implements elevator door state judgment, passenger attribute recognition, and duplicate-free traffic counting through re-recognition in order to give demand-based operational strategies and recommendations. By monitoring the elevator occupancy on a typical weekday, the elevator operation pattern is classified into three periods including peak period, idle period and balance period, and the demand-driven operation optimization is guided based on their corresponding characteristics. Experiments show that the proposed deep learning approach exhibits high accuracy in all stages of the pipeline. IEIS can facilitate energy-efficient management of elevators, ensure safety, provide human-centered services, and reduce the cost of human intervention while achieving efficient operation, and is expected to migrate to other scenario applications, bringing multiple potential benefits to the society.

Designing a hybrid intelligent transportation system for optimization of goods distribution network routing problem

Given that finding the right and appropriate route in the daytime and busy city with the occurred traffic limitations is a major problem that not only causes inefficient performance in distribution networks but also causes irreparable environmental damage to society. This study focuses on improving the routing of the goods distribution network using the intelligent transportation system. In this regard, first, the problem is modeled, and then an intelligent transportation system is combined with some meta-heuristic algorithms to solve it. In the proposed algorithm, we first use the clustering algorithm to cluster location of customers and then create sub-clusters based on the time window. The proposed routes are created by using the genetic and particle swarm optimization meta-heuristic algorithms as the static part of the approach, and if the traffic conditions change, the Vehicular Ad - hoc Network (Vanet), which is one of the sub-systems of the intelligent transportation system as the dynamic part of the approach checks the new traffic conditions and sends the new information to the proposed algorithms to recheck the route. The Aarhus-Denmark data set is selected due to having urban traffic information, meteorology, and urban areas. This is related to the City Pulse project. According to the obtained results, in terms of reducing the cost of transmission, including the cost of service delay and total cost of moving, the proposed method reached better solutions comparing to the meta-heuristic algorithms of literature.

Adaptive Neuro-Fuzzy Optimization for Enhanced Precision in Laser Micro-Machining Operations

In this study, an intelligent optimization system for laser micro-machining operations is developed, utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The heuristic optimization tool, ANFIS, synergistically combines back-propagation training with gradient descent in a unidirectional manner. A comprehensive training set, incorporating experimental data from the literature, highlights the sensitivity of groove depth and recast layer height to specific critical operating factors during the laser micro-machining process. By optimizing lamp current, pulse width, and frequency, the proposed system aims to achieve superior groove depth and recast layer height outcomes. This novel microscopic research holds the potential to captivate both academic scholars and industry professionals.

Developing Machine Learning-Based Intelligent Control System for Performance Optimization of Solar PV-Powered Refrigerators

Display refrigerators consume significantly high energy, and improving their efficiency is essential to minimize energy consumption and greenhouse gas emissions. Therefore, providing the refrigeration system with a reliable and energy-efficient mechanism is a real challenge. This study aims to design and evaluate an intelligent control system (ICS) using artificial neural networks (ANN) for the performance optimization of solar-powered display refrigerators (SPDRs). The SPDR was operated using the traditional control system at a fixed frequency of 60 Hz and then operated based on variable frequencies ranging from 40 to 60 Hz using the designed ANN-based ICS combined with a variable speed drive. A stand-alone PV system provided the refrigerator with the required energy at the two control options. For the performance evaluation, the operating conditions of the SPDR after the modification of its control system were compared with its performance with a traditional control system (TCS) at target refrigeration temperatures of 1, 3, and 5 °C and ambient temperatures of 23, 29, and 35 °C. Based on the controlled variable frequency speed by the modified control system (MCS), the power, energy consumption, and coefficient of performance (COP) of the SPDR are improved. The results show that both refrigeration control mechanisms maintain the same cooling temperature, but the traditional refrigerator significantly consumes more energy (p < 0.05). At the same target cooling temperature, increasing the ambient temperature decreased the COP for the SPDR with both the TCS and MCS. The average daily COP of the SPDR varied from 2.8 to 3.83 and from 1.91 to 2.82 for the SPDR with the TCS and MCS, respectively. The comparison results of the two refrigerators’ conditions indicated that the developed ICS for the SPDR saved about 35.5% of the energy at the 5 °C target cooling temperature and worked with smoother power when the ambient temperature was high. The COP of the SPDR with the MCS was higher than the TCS by 26.37%, 26.59%, and 24.22% at the average daily ambient temperature of 23 °C, 29 °C, and 35 °C, respectively. The developed ANN-based control system optimized the SPDR and proved to be a suitable tool for the refrigeration industry.

Autonomous Recharging and Flight Mission Planning for Battery-Operated Autonomous Drones

Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their onboard batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re- computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments. Note to Practitioners—This study is stimulated by the need for developing pragmatic and provably efficient automated tour management systems for UAVs deployed on energy-constrained, long-distance flight missions. As such, UAVs provide a nifty platform for facilitating environmental monitoring, disaster management, transport of medical supplies, as well as expediting last-mile deliveries. However, existing path planners generally fall short of capturing several crucial aspects, such as detailed power consumption model (e.g., factoring in payload, wind speed and direction) or performance guarantees, potentially leading to underutilized or infeasible routing decisions. To address these issues, the present work proposes a theoretically-backed routing approach with a certifiable degree of optimality and develops an effective, practical power consumption evaluation model for multi-rotor UAVs, verified on multiple drone models.

Intelligent optimization system of burden structure in sintering and blast furnace ironmaking process based on improved genetic algorithm

ABSTRACT Under the premise of ensuring the smooth production of the blast furnace, the quality and output of molten iron, minimizing the cost of hot metal and reducing the impact of harmful elements are the most difficult problems faced by iron and steel enterprises at present. The intelligent optimization system of burden structure in the whole process of sintering and blast furnace ironmaking is developed based on the principle of blast furnace ironmaking and material balance, improved genetic algorithm and penalty function. It includes four parts: raw fuel database, intelligent sintering optimization, blast furnace burden structure intelligent optimization and company cost accounting. The industrial application shows that the system can not only guarantee the output and quality of molten iron, but also effectively reduce the cost of molten iron in the process of optimizing the sintering and blast furnace production of the steel company.

Intelligent Cost Accounting Optimization System of Building Engineering Materials Based on Ant Colony Algorithm

To save money on labour and keep track of the expenses of building engineering materials. This study provides an intelligent accounting and optimization system for building materials costs based on the ant colony algorithm. In the hardware, three relays are used to achieve open phase protection and convert the voltage signal into current signal; in the software, distinguish the material characteristics, identify the material types, use the virtual governance cost method, optimise the cost intelligent accounting mode, and design the software integration management function using integration technology and the ant colony algorithm; and in the hardware, use the virtual governance cost method. The average labour cost of the intelligent building material cost accounting and optimization system in this paper and the other three systems is 1924 (RMB 10,000), 2749 (RMB 10,000), and 2744 (RMB 10,000), respectively, demonstrating that the intelligent building material cost accounting and optimization system integrated with the ant colony algorithm has a better overall performance.

An intelligent system for blood donation process optimization - smart techniques for minimizing blood wastages

An intelligent system for blood donation process optimization - smart techniques for minimizing blood wastages

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid-Structure Interaction.

Based on expert system theory and fluid–structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid–structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system.

INTELLIGENT OPTIMIZATION SYSTEM FORMULATED ON INDUSTRIAL INTERNET PLATFORM

The opinion of "Industrial Internet" was first suggested by General Electric. As a modern infrastructure of the 4 -th industrial revolution, Industrial Internet has become a substantial mechanism to actualiza digital revision of industrial economy. The Industrial Internet platform is a system formulated on huge data gathering, accumulation and analysis, which is based to the necessity of digitalization, networking and philosophizing of manufacturing industry. In order to settle the problems of actual rearward industrial data, improve the capacity of data acquisition and deal with the industrial interconnection platform, the industrial intelligent optimization system generates the product services needed by industrial enterprises and realizes the object of industrial optimization. Key words: Industrial Internet; industrial intelligence; industrial mechanism

RETRACTED ARTICLE: Residential planning and layout design method based on multi-sensor information fusion

With the in-depth development of the market economy and the acceleration of the process of urbanization, a large number of people have poured into the city, and a large number of residential communities have been developed and constructed. Design quality in the development process is often replaced by output. Some companies attach importance to quality but do not know how to control quality. Based on this, this paper uses the method of multi-sensor information fusion to study the planning and layout design methods of residential areas, and provides a design basis for solving the problems of people’s living environment deterioration and increasingly scarce land resources. Based on the Rhinoceros and Grasshopper parametric platform, this paper integrates residential information model, performance prediction technology, and multi-sensor information fusion technology, taking residential planning and layout parameters as design variables. A set of intelligent optimization system for residential planning and layout based on multi-performance objective simulation was compiled. The “internal factors” and “external factors” that affect the results of RLIOS are studied, and then the residential planning and layout design methods are studied. Experiments have proved that no matter what algorithm is used, the performance of each target can be improved, the floor area ratio performance can be improved by 35.3–3.3%, and the open space performance can be improved by 12.6–31.36%. It shows that the residential planning and layout design method based on multi-sensor information fusion proposed in this paper improves the accuracy and efficiency of the work.

Fog-based Intelligent Transportation System for Traffic Light Optimization

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

Intelligent energy optimization system development and validation for German building types

Abstract Cost-effective building energy optimization solutions have a strategic function to assist German and European energy policies for the simple reason that buildings account for 40% of the total energy consumption in Europe. The International Energy Agency considers energy efficiency as the first fuel and a major resource. In this work, a cost-effective building energy optimization system consisting of 14 different wireless network embedded sensors, control units (actuators), wireless communication protocol, multi-dimensional information management system, context sensitive graphical user interfaces and energy optimization software with artificial intelligence-backed control algorithms is examined. The R&D activity is accomplished in the ‘Intelligent Building Energy Management System’ research project, which is funded by the State of Lower Saxony (Germany) in the frame of Innovation Support Program between the years 2014 and 2018. The ‘Energy Optimization System (EOS)’, which is subject to this research, reduces the total energy use and CO2 emissions of buildings through optimization of energy-consuming building systems, such as heating, cooling, lighting systems and home/office appliances with the support of an integrated system solution consisting of artificial intelligence-based software and wireless network embedded hardware. The system is deployed and validated in two appropriately selected test buildings in Germany. It has been recorded that the system provides energy efficiency levels between 29.34% and 38.18% under different seasonal and occupancy conditions in office and residential building types. The developed system can be deployed to all types of historical and new buildings to optimize energy consumption and reduce carbon emissions.