Intelligent Optimization Method Research Articles

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.

Optimization of embedded sensor placement for small satellites

Abstract Dynamic environment testing is important in the manufacturing phase of a satellite before launch. However, redundancy is introduced into the testing by the massive placement of vibration sensors inside and outside the satellite, adversely affecting testing efficiency, especially in the development of satellite constellations. An intelligent algorithm-based sensor optimization method is proposed to maximize the relationship between measure points and reduce the number of internal sensors while ensuring the independence of external sensors. A frequency response matrix of internal and external sensors is constructed through a frequency-sweeping analysis of the satellite structure. Subsequently, external sensors are categorized into different groups by using principal component analysis (PCA) and K-means clustering analysis. A modified information entropy approach is applied to optimize the quantity of internal sensors. Through the practical application of sensor reduction in a specific satellite, the proposed method achieves a good reduction result.

Intelligent design of multi-layered variable stiffness composite structure based on transfer learning

Variable stiffness composite structures offer more flexible design space than thin-walled metal structures and have greater potential for vibration-resistant design. When faced with multiple new types of design problems, the complex modelling and analysis procedures frequently prove to be both time-consuming and costly in terms of optimization. In this study, an innovative multi-layered variable stiffness (MVS) composite structure with high design flexibility is proposed, with images representation for curvilinearly stiffened paths, non-uniform layouts, and fiber and layup angles. Moreover, an intelligent optimization method based on transfer learning is proposed for addressing a variety of factors affecting dynamic design, including boundary types, structural features, and dynamic responses. The objective of the transfer learning model is to facilitate the inheritance and sharing of variable stiffness features, thereby enabling the efficient design of new problems with limited datasets. The validation of different examples shows that the transfer learning can effectively acquire the structural features from the existing source domain datasets, thereby significantly reducing the data for some new target domains by approximately 50 %. In comparison to the initial constant stiffness (CS) structures, the different optimized configurations indicate that the MVS composite structures are capable of effectively enhancing the dynamic responses by 10 %∼146 % for natural frequency and dynamic compliance. Furthermore, the MVS optimized configuration displays superior dynamic responses in some problems, when compared to the CS optimized configuration.

Feasibility of optimum energy use and cost analyses by applying artificial intelligence and genetic optimization methods in geothermal and solar energy-assisted multigeneration systems

This study evaluates the potential for optimizing energy utilization and cost analysis in geothermal and solar energy-supported multigeneration systems using artificial intelligence (AI) and genetic algorithm (GA) optimization methods. Integrating renewable energy sources aims to promote sustainable energy solutions focusing on efficiency and cost-effectiveness. A cogeneration system that leverages geothermal and solar energy for electricity generation and space cooling is analyzed. The model incorporates an organic Rankine cycle (ORC) for power generation and a geothermal-solar absorption cooling cycle for space cooling, developed using Artificial Neural Networks (ANN) and optimized through thermoeconomic methods. Thermodynamic and thermoeconomic analyses guide the optimization process using an ANN-based GA method. The system, with parameters such as a 130 °C geothermal source temperature, an 85 kg/s mass flow rate, and a 600 W/m2 monthly average solar radiation intensity in Afyonkarahisar, achieves overall energy and exergy efficiencies of 19.5 % and 43.5 %, respectively. Optimization via the GA method significantly improves performance, increasing net power output from 2240 kW to 2760 kW and cooling capacity from 2720 kW to 3061 kW. Additionally, the cost of electricity decreases by 12.1 %, from 0.0165 $/kWh to 0.0145 $/kWh, while the cooling cost is reduced by 41.9 %, from 0.0745 $/kWh to 0.0525 $/kWh.

Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application.

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm's optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy's efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms.

Intelligent accounting optimization method based on meta-heuristic algorithm and CNN.

The evolution of social intelligence has led to the adoption of intelligent accounting practices in enterprises. To enhance the efficiency of enterprise accounting operations and improve the capabilities of accountants, we propose an intelligent accounting optimization approach that integrates meta-heuristic algorithms with convolutional neural networks (CNN). First, we enhance the CNN framework by incorporating document and voucher information into accounting audits, creating a multi-modal feature extraction mechanism. Utilizing these multi-modal accounting features, we then introduce a method for assessing accounting quality, which objectively evaluates financial performance. Finally, we propose an optimization technique based on meta-heuristic principles, combining genetic algorithms with annealing models to improve the accounting system. Experimental results validate our approach, demonstrating an accuracy of 0.943 and a mean average precision (mAP) score of 0.812. This method provides technological support for refining accounting audit mechanisms.

Logistics Routing Intelligence based on Improved Ant Colony Algorithm and Dijkstra Algorithm

As the domestic logistics industry still faces challenges in terms of efficiency and cost-effectiveness, the optimization of logistics routes is crucial to address these challenges. The purpose of this study is to explore the potential of using ant colony algorithm and route optimization algorithm to realize intelligent optimization of logistics routes. It first introduces the principle of ant colony algorithm and its application in path optimization, and then proposes a path intelligent optimization method based on ant colony algorithm by introducing pheromone concentration and heuristic information, which improves the efficiency and accuracy of the algorithm. Finally, the experimental results show that the improved ant colony algorithm can better plan the path than the traditional ant colony algorithm, and can improve the convergence speed of calculation, and can achieve higher optimization accuracy and calculation efficiency, so that it is suitable for the practical application in the field of logistics path intelligent optimization.

Grasshopper platform-assisted design optimization of fujian rural earthen buildings considering low-carbon emissions reduction

This work aims to explore optimization methods for the design of earthen buildings in rural Fujian to achieve low-carbon emissions and improve the structural stability of earthen buildings. First, parametric modeling and optimization algorithms are employed through the Grasshopper platform. An intelligent earthen building design is created by combining the optimization of factors such as the structure of earthen buildings, building materials, and orientation. Then, a comparison is made with the unoptimized, energy-efficient, and carbon emission reduction designs. Finally, the work concludes that the proposed design significantly optimizes the total carbon emissions, energy consumption, structural stability, and economic aspects. The proposed design scheme achieves the highest carbon emission reduction effect, with a reduction rate of 34.64%. The proposed design exhibits lower maximum stress and higher minimum safety factor in terms of structural stability compared to other scenarios, along with smaller structural displacement. It also performs well in terms of initial investment, annual operating costs, and construction period. The significance of this work lies in providing scientific guidance for the design and construction of rural earthen buildings, promoting the organic integration of rural development with low-carbon initiatives. This indicates that the use of intelligent optimization methods for earthen building design is feasible and can yield positive results in practice.

Optimization Effectiveness of Multi-Intelligence Consistent Energy System Based on Digital Advertising and Data Analysis

This paper introduces a novel approach to address the issue of overestimation of state-action values in reinforcement learning algorithms based on the Q-framework. It integrates a dual-layer Q-learning algorithm with a grey wolf intelligent optimization method, enabling rapid search for optimal allocations in unknown search spaces. This integration results in the development of a multi-agent collaborative Area-Grid Coordination (A-GC) strategy, termed the grey wolf double Q (GWDQ) strategy, tailored for multi-area energy interconnection scenarios. The proposed GWDQ strategy is evaluated through simulation experiments on comprehensive energy system models, including mixed gas turbine systems, Combined Cooling, Heating, and Power (CCHP) systems, and a multi-area energy-interconnected Northeast power grid model. A centralized architecture is established to analyze the optimization effects of digital advertising. The performance of the GWDQ strategy is compared with traditional reinforcement learning algorithms through simulation and empirical data validation. Results indicate that the GWDQ strategy exhibits stronger learning capabilities, improved stability, and enhanced control performance compared to traditional methods. It demonstrates superior optimization for digital advertising and enables swift acquisition of optimal coordination in A-GC processes across multiple regions. Additionally, the paper analyzes the environmental and economic impacts of the proposed strategy.

Analytical model and flow velocity control of electrohydrodynamics system with multi-needle corona discharge

For the flow field distribution and control mechanism generated by the electrohydrodynamics (EHD) system with multi-needle corona discharge, this paper takes the multi-needle EHD pump as the research object, establishes different types of physical models through regional division, constructs multi-physical field coupling relationship, and derives a simplified EHD flow velocity equation suitable for the EHD system with multi-needle corona discharge. Combined with the intelligent optimization method of population evolution, a novel and effective intelligent algorithm is designed for the numerical analysis of the velocity profile distribution of a multi-needle EHD pump, and the flow velocity control law of the multi-needle EHD pump is analyzed by quantitative calculation. The validity of the model and analysis is verified by the electric field and flow field simulation of the multi-needle EHD pump system. The calculation results show that the voltage parameter is more dominant than the electrode spacing parameter in the steady-state flow velocity control of the multi-needle EHD pump, and both the maximum flow velocity and the average flow velocity are superlinearly controlled by voltage. In the design of multi-needle EHD pump with an electrode spacing of 1 cm, the simulation results show that the maximum gas flow velocity of 0.82 m/s can be obtained by providing 5000 V voltage, which verifies the design of a miniaturized multi-needle EHD pump and its feasibility in gas lasers and other application scenarios.

Comparative Evaluation of the Application Effectiveness of Intelligent Production Optimization Methods in Offshore Oil Reservoirs

The development of offshore oil fields confronts challenges associated with high water cut and low displacement efficiency. Reservoir injection-production optimization stands out as an effective means to reduce costs and enhance efficiency in offshore oilfield development. The process of optimizing injection and production in offshore oil reservoirs involves designing strategies for a large number of wells and optimization time steps, constituting a large-scale, complex, and costly optimization computation problem. In recent years, with the rapid advancements in big data and artificial intelligence technologies, sophisticated evolutionary computation methods have found widespread application in reservoir injection-production optimization problems. However, the abundance of intelligent optimization algorithms raises the question of how to choose a method suitable for the complex optimization background of offshore oilfield injection-production optimization. This paper provides a detailed overview of the application of an existing differential evolution algorithm (DE), conventional surrogate-assisted evolutionary algorithm (CSAEA), and global–local surrogate-assisted differential evolution (GLSADE) in the context of practical offshore oilfield injection-production optimization problems. A comprehensive comparison of their performance differences is presented. The study concludes that the global–local surrogate-assisted evolutionary algorithm is the most suitable method for addressing the current challenges in offshore oilfield injection-production optimization.

Research on Intelligent Control and Optimization Methods in Power Systems

Research on Intelligent Control and Optimization Methods in Power Systems

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.

Application of some artificial intelligence optimization methods to determine the freshness of eggs

Application of some artificial intelligence optimization methods to determine the freshness of eggs

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm.

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

An intelligent optimization method for the facility environment on rural roads

AbstractThis study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time‐consuming, this method can rapidly generate optimized visual images of the facility environment and promptly verify the optimization effects. In this study, a visual road schema model is established to quantify the facility environment from drivers’ visual perception, and an automated optimization scheme determination approach considering the original facility environment characteristics is proposed using self‐explaining theory. Then, Cycle‐consistent generative adversarial network is used to automatically generate optimized facility environment images. To verify the optimization effect, operation speeds of the optimized facility environments are predicted using random forest. The case study shows that this method can effectively optimize the facility environment where original operation speeds are more than 20% over the speed limits, and the whole process only takes 1 h far less than several months or years in previous ways. Overall, this study advances the intelligence level in optimizing the facility environment and enhances rural road safety.

Efficient topology reconfiguration for NoC-based multiprocessors: A greedy-memetic algorithm

In multi-core processor systems, the Network-on-Chip (NoC) serves as a vital communication infrastructure. To ensure chip reliability during potential failures, this paper proposes a two-level topology reconfiguration algorithm with core-level redundancy technology. Initially, a heuristic topology reconfiguration method utilizing a greedy strategy is proposed to perform local replacement of faulty processing elements (PEs) and generate an initial logical topology with shorter interconnection paths between PEs. Then, an intelligent optimization method based on memetic algorithm is introduced to optimize the generated initial topology for better communication performance. The experimental results demonstrate that compared to the current state-of-the-art algorithm, the proposed algorithm achieves an average improvement of 13.92% and 30.83% on various size topologies in terms of distance factor (DF) and congestion factor (CF), which represent communication delay and traffic balance respectively. The proposed algorithm significantly enhances the communication performance of the target topology, mitigating communication latency and potential congestion problems.

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

An artificial intelligence optimization method of back analysis of unsteady-steady seepage field for the dam site under complex geological condition

An artificial intelligence optimization method of back analysis of unsteady-steady seepage field for the dam site under complex geological condition