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optimization Technology of Combustion Engine Control Based on swarm Intelligent optimization algorithm and Improved Clustering algorithm

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IEEE ACCESS 2024年 12卷 121596-121609页

作者： Wang, Jiadong Zhang, Ming Li, Jinfeng Guoneng Beijing Gas Fired Cogenerat Co Ltd Beijing 100000 Peoples R China

Since control instructions are the fundamental component of thermal power generation, the quality and effectiveness of implementation directly affect the efficiency of the energy system. In order to improve the efficiency of internal combustion engine control, an optimization method of internal combustion engine control based on enhanced clustering algorithm and swarm intelligence optimization algorithm is proposed. The process simplifies the main structure of the gas turbine, divides the combustion engine model into multi-input single-output systems, and introduces the artificial bee colony algorithm to optimize the parameters. A new nectar search formula is constructed by using the global optimal nectar, and the control parameters are calculated by fuzzy logic clustering. The experimental results showed that the modeling error of the load model of internal combustion engine was in the range of -0.47 MW similar to 0.51 MW. When the training iteration speed was tested, the loss value of the research method dropped rapidly in the first 10 iterations. When analyzing the change of the control quantity during load change, if the exhaust flow rate was taken as the control quantity, the control results of the research method was always kept within 5lbm/s error. It demonstrates that this research method can effectively improve the running quality of the internal combustion engine and has a good running efficiency. The research can provide certain technical reference for gas turbine control in thermal power generation.

关键词： Combustion engine control swarm intelligence optimization algorithm clustering algorithm fuzzy logic fireworks algorithm

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FineAP: Fine-Grained Access Point Deployment Strategy for 60 GHz Millimeter-Wave Wireless Networks

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IEEE COMMUNICATIONS LETTERS 2023年第1期27卷 381-385页

作者： Yang, Yi Zhou, Anfu Ma, Huadong Henan Univ Anim Husb & Econ Sch Informat Engn Zhengzhou 450045 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China

Traditional access point (AP) deployment is based on the principle of minimum area coverage, which takes the AP signal range as the radius and performs a geometric calculation to determine the deployment location. These methods lack support for various contexts and requirements, making them only useful in a conventional rectangular environment. As a result, we design a fine-grained AP deployment (FineAP) strategy, which first uses ray-tracing to determine the signal distribution of various locations in the environment, and then uses a hybrid swarm intelligence optimization algorithm based on the principle of signal matrix maximization to determine the AP deployment location. When compared to traditional deployment methods, FineAP can handle a variety of application scenarios with close to upbound performance and the least minimum of overhead.

关键词： Millimeter wave communication Loss measurement Manganese Iterative methods Propagation losses Relays Ray tracing 60 GHz millimeter wave networks signal coverage access point deployment ray-tracing swarm intelligence optimization algorithm

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A microbial treatment population dynamics optimization approach

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 4035-4049页

作者： Gao, Jinding Hunan Int Econ Univ Acad Affairs Off Changsha 410205 Peoples R China

In order to solve some function optimization problems, Population Dynamics optimization algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500 similar to 1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

关键词： swarm intelligence optimization algorithm population dynamics environmental pollution microbial control

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Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Meng, Guanglei Cong, Zelin Li, Tingting Wang, Chenguang Zhou, Mingzhe Wang, Biao Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China Aviat Sci & Technol Key Lab Air Combat Syst Techno Shenyang 110136 Peoples R China

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining spec

关键词： Dynamic Bayesian networks Structural learning swarm intelligence optimization algorithm Bacterial foraging optimization algorithm

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The analysis and re-optimization of food systems by using intelligent optimization algorithms and machine learning

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ALL LIFE 2022年第1期15卷 656-677页

作者： Wang, Xu Jilin Univ Changchun 130000 Peoples R China

Facing the serious concern of global food security, this study focuses on the feasibility of improvements to food systems and predicts future changes. This study proposes a coordination evaluation index incorporating diverse indicators of different food systems, analyzes the changes after modification, and predicts the equilibrium time and the critical point. In this regard, we pre-processed the data using the entropy method, variance contribution rate, and normalization. Instead of utilizing single-element linear forecasting and economic income and expenditure models, we innovatively developed a multivariate evaluation system for population, cultivated land, and food systems in three major directions. Meanwhile, after conducting a cross-sectional comparison of the prediction effects of various algorithms, we finally selected Gaussian process regression and a neural network to build a prediction model to develop food systems of different sizes. After establishing the evaluation index and development prediction model, we fitted the three-dimensional surface of the developmental change using thin-plate interpolation. We adopted the swarm intelligence optimization algorithm to search for the balance and critical points after the change. We also compared various swarm intelligence optimization algorithms, such as the particle swarm optimization algorithm, salp swarm algorithm, and whale optimization algorithm.

关键词： Re-optimizing food systems Multiple Regression Analysis coupling degree evaluation model swarm intelligence optimization algorithm Neural Network

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Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation algorithms

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Fluid Dynamics & Materials Processing 2023年第6期19卷 1693-1708页

作者： Zhonghai Jiang Qian Wang Liangbing Zhou Chun Xiao School of Transportation and Civil Architecture Foshan UniversityFoshan528000China Guangdong Yuanshun Architectural Design Co. Ltd.Foshan528000China Eternal Estate Engineering Design Co. Ltd.Chengdu610017China

A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt *** calculation of each layer only needs four iterations to achieve ***,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with *** the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the *** shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model.

关键词： Asphalt pavement temperature field swarm intelligence optimization algorithm prediction

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The Variation Step Adaptive Glowworm swarm optimization algorithm in Optimum Log Interpretation for Reservoir with Complicated Lithology 12

The Variation Step Adaptive Glowworm Swarm Optimization Algo...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Mo, Xiuwen Li, Xiao Zhang, Qiang Jilin Univ Coll Geoexplorat Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781509040933

Inversion of geophysical logging data is one of the most important tasks in oil and gas exploration. Ambiguity is usually inherent for the solutions, especially for formation with complex lithology. The optimum log interpretation technique can effectively reduce the ambiguity of the interpretation results. Therefore, the Glowworm swarm optimization (GSO), one of the swarm intelligence optimization algorithms, is introduced into the log interpretation to obtain the optimal solution by virtue of its strong ability both in local and global optimization. Moreover, in order to solve the problem of slow convergence speed in the later iteration process, adaptive step is integrated into glowworm swarm optimization to form the Variation Step Adaptive Glowworm swarm optimization (VSAGSO) algorithm, which improves the accuracy and efficiency of optimizing. VSAGSO algorithm is applied for test in the tuffaceous sandstone reservoir in a certain oilfield. Comprehensively considering all kinds of errors and constraints, it could directly working-out the optimized results of reservoir parameters such as tuff content, shale content, skeleton mineral content and porosity in well accordance with the core data.

关键词： optimum well logging interpretation glowworm swarm optimization algorithm swarm intelligence optimization algorithm self-adaption tuffaceous siltstone reservoir

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An Improved Hippopotamus optimization algorithm Utilizing swarm-Elite Learning Mechanism's Levy Flight and Quadratic Interpolation Operators for Optimal Parameters Extraction in Photovoltaic Models 20th

An Improved Hippopotamus Optimization Algorithm Utilizing Sw...

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20th International Conference on Intelligent Computing (ICIC)

作者： Wang, Wentao Tian, Jun Nankai Univ Coll Software Tianjin 300350 Peoples R China

ISBN: (纸本)9789819755776;9789819755783

Photovoltaic (PV) systems are among the representatives of renewable energy technologies, and their performance is influenced by parameter configurations. This paper utilizes the swarm-elite learning mechanism's Levy flight and Quadratic interpolation strategies to enhance the optimization performance of the Hippopotamus optimization algorithm (HO) in both the exploration and exploitation stages. The proposed algorithm is referred to as LQHO. The aim of this paper is to utilize LQHO to provide a high-quality solution for parameter extraction problems in various types of PV models. Ten representative CEC2017 functions and three PV models are selected for designing experiments to evaluate the optimization performance and parameter extraction capability of LQHO. Five advanced metaheuristic algorithms are chosen to design control group experiments. The results indicate that LQHO exhibits superior performance over its competitors in both the parameter extraction problems of the tenCEC2017 functions and the three PV models. This superiority is reflected in terms of solution accuracy, convergence speed, and robustness.

关键词： Photovoltaic Model Hippopotamus optimization algorithm Clean Energy swarm intelligence optimization algorithm

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FRACTIONAL ORDER CHAOTIC MODEL BASED ENHANCED EQUILIBRIUM optimization algorithm FOR CONTROLLER DESIGN OF 3 DOF HOVER FLIGHT SYSTEM 17

FRACTIONAL ORDER CHAOTIC MODEL BASED ENHANCED EQUILIBRIUM OP...

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ASME International Design Engineering Technical Conferences / 41st Annual Computers and Information in Engineering Conference (IDETC-CIE) / 17th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Ates, Abdullah Chen, YangQuan Inonu Univ Dept Comp Engn Malatya Turkey UC Merced Mechatron Embedded Syst & Automat Lab MESALab Merced CA USA

ISBN: (纸本)9780791885437

In this study, the K feedback gain vector parameters that are used for the control of three degree of freedom four rotor quadcopter system (3 DOF Hover) are optimized with the Enhanced Equilibrium optimization algorithm ((EO)-O-2). The (EO)-O-2 algorithm is proposed with using parameters obtained from fractional order chaotic oscillator models instead of random variables. The Basic EO algorithm is inspired by volume- mass balance. In EO algorithm, each particle is called a motion that searches a parameter vector space. However, random coefficients derived from uniform distribution are used in the parameters updating process or in the generation of the initial population. The (EO)-O-2 algorithm was proposed by using vectors obtained from fractional order chaotic oscillators instead of stochastic coefficients in the basic Equilibrium optimization algorithm. Genesio Tesi, Rossler, Lotka Volterra fractional-order chaotic oscillator models were used in the (EO)-O-2 algorithm to optimize K feedback gain vector of 3 DOF Hover. The order and initial conditions the fractional chaotic oscillator models were experimentally adjusted for the control of 3 DOF problem. Thus, suitable fractional-order chaotic models for the problem were obtained. The (EO)-O-2 algorithm results are compared with the Stochastic Multi Parameter optimization (SMDO) and Discreet Stochastic optimization (DSO) algorithms for the system's pitch, roll and yaw angles.

关键词： Fractional Calculus swarm intelligence optimization algorithm Control

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A multi-strategy fusion dung beetle optimization algorithm 24

A multi-strategy fusion dung beetle optimization algorithm

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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Li, Yihang Lv, Zhimin Univ Sci & Technol Beijing Collaborat Innovat Ctr Steel Technol Beijing Peoples R China

ISBN: (纸本)9798400716751

A recently developed swarm intelligence method that converges more quickly than some existing algorithms is called the dung beetle optimization algorithm (DBO). However, the algorithm still suffers from the disadvantage that the initial population is not uniform and easily falls into the global optimal solution. In this paper, a multistrategy fusion dung beetle optimization algorithm (MSFDBO) is proposed which is inspired by the sine search algorithm, whale optimization algorithm, and reverse learning algorithm. First, the initial population of dung beetles is initialized using the singer chaos function to obtain a more uniform initial population. In view of the rolling behavior of dung beetles, adaptive coefficients and sine search strategies are introduced to enhance the search range of the algorithm, and the amplitude of the sine function is changed through the adaptive coefficients to balance the local and global searches of the algorithm. In view of the foraging behavior, egg-laying behavior and stealing behavior of dung beetles, an adaptive spiral search strategy is introduced to improve the optimization ability of the algorithm in the solution space. For the optimal solution obtained in each iteration, a convex lens imaging reverse learning strategy is used to perturb the optimal solution to avoid the dung beetle population from falling into a local optimum during the iteration process. By comparing the benchmark test function and the Wilcoxon rank sum test, MSFDBO shows better convergence performance, optimization performance, and robust performance.

关键词： Dung beetle optimizer singer mapping sine optimization algorithm whale optimization algorithm swarm intelligence optimization algorithm reverse learning strategy

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