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4th International Conference on Robot Systems and Applications (ICRSA)

作者： Li, Jun Dai, Wenrui Hu, Lei Army Acad Artillery & Air Def Beijing Peoples R China

ISBN: (纸本)9781450384940

Whether the air defense radar network layout is reasonable or not directly affects the combat effectiveness. The traditional layout method is mainly based on manual layout, which is greatly limited, so it is difficult to maximize the combat effectiveness of the whole system. In this paper, an improved particle swarm optimization algorithm is proposed to solve the problem of radar network layout optimization, which has the advantages of high speed and high precision. It can not only meet the needs of real-time simulation of electronic air defense operations, but also be used to assist commanders in decision-making in actual battlefield.

关键词： intelligent optimization algorithm Air defense radar network Station layout optimization

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intelligent optimization Method for Core Flow Zoning of Long-Cycle Lead-Bismuth-Cooled Reactor

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Yu, Qingyuan Dai, Shengqi Zhao, Pengcheng Zhao, Yanan Xiao, Yingjie Peng, Liangxing Yu, Tao Univ South China Sch Nucl Sci & Technol Hengyang Peoples R China Nucl Power Inst China Sci & Technol Reactor Syst Design Technol Lab Chengdu Peoples R China

Flow zoning is an important way to achieve core outlet temperature flattening. Appropriate zoning can improve safety and economy. This study combines an artificial intelligence optimization algorithm with a parallel multi-channel model to develop a model for calculating reactor core flow zoning based on the modern optimization theory, convergence analysis of a genetic algorithm, differential evolution algorithm, and quantum genetic algorithm is carried out for long-life reactor flow partitioning. Using the optimized algorithm, two flow rates are determined using power distribution at the beginning of the core life as the sample data and the maximum power of each fuel assembly during the entire life as the sample data. Comparative analysis of two different flow zoning schemes is implemented on a small long-life natural circulation lead-bismuth fast reactor, SPALLER-100. The findings of this study show that the quantum genetic algorithm has the best convergence for the long-life reactor among the three intelligent optimization algorithms, and it can quickly provide optimal results. In flow zoning scheme calculations based on the core power distribution at the beginning of reactor life, the maximum outlet temperature of the fuel assembly exceeds the thermal safety limit of the reactor, and in the flow zoning scheme calculations based on the average core power distribution during the whole reactor life, the maximum outlet temperature of the fuel assembly is 140 K lower than the maximum outlet temperature obtained in the previous scheme, remaining below the thermal safety limit. The optimal number of partitions for the SPALLER-100 reactor is determined to be 5, and increasing the number of zones only slightly improved the thermal safety performance of the reactor.

关键词： lead-bismuth reactor flow zoning intelligent optimization algorithm quantum genetic algorithm safety performance

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Integrating socioecological indexes in multiobjective intelligent optimization of green-grey coupled infrastructures

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RESOURCES CONSERVATION AND RECYCLING 2021年 174卷 105801-105801页

作者： Liu, Zijing Xu, Changqing Xu, Te Jia, Haifeng Zhang, Xiang Chen, Zhengxia Yin, Dingkun Tsinghua Univ Sch Environm Beijing 100084 Peoples R China Wuhan Univ Sch Water Resources & Hydropower Engn Wuhan 430072 Peoples R China

Ecological conservation is an important objective in urban runoff management today. Maintaining a sustainable ecological system is as equally important a task as to ensure the safety of drainage systems and runoff management cost control. Thus, the socioecological influences of runoff control infrastructure are innovatively included in a uniform evaluation framework with the control functions and capital investments in this study. These indexes are quantified through hydrological model simulation, life cycle cost analysis, and life cycle assessment. Traditional grey infrastructure and rapidly developing green infrastructure for runoff control are optimized simultaneously. For the trade-off between multiobjectives and configurations of multi-infrastructures, nondominated sorting genetic algorithm-II is utilized to achieve automatic optimization of runoff control infrastructure scale, thereby avoiding the dilemma where manually arranged schemes cannot perform optimally. This multiobjective intelligent optimization is applied to a sponge city pilot region in Wuhan, China, and tradeoffs are made in the Pareto optimal solution set. A breakthrough is claimed in quantifying the respective contribution of green and grey infrastructures to the optimal scenario in terms of runoff control function, cost input, and socioecological influence. For socioecological influence, the paybacks can meet the investment in the aspect of toxicity health hazard, pathogenic matter, global warming, terrestrial acidification, and water eutrophication (average socioecological paybacks are 2.0, 2.1, 2.9, 1.9, and 2.1 times to the investments respectively). Results prove the necessity of considering multiobjective optimization and green-grey couple infrastructures in a uniform framework.

关键词： intelligent optimization algorithm Multiobjective evaluation system Green-grey coupled infrastructure Hydrological model simulation Life cycle cost Life cycle assessment

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A Survey on intelligent optimization Approaches to Boiler Combustion optimization

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CAAI Artificial Intelligence Research 2023年第1期2卷 16-31页

作者： Jing Liang Hao Guo Ke Chen Kunjie Yu Caitong Yue Yunpeng Ma School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang 471000China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453000China School of Information Engineering Tianjin University of CommerceTianjin 300134China

This paper reviews the researches on boiler combustion optimization,which is an important direction in the field of energy saving and emission *** methods have been used to deal with boiler combustion optimization,among which evolutionary computing(EC)techniques have recently gained much ***,the existing researches are not sufficiently focused and have not been summarized *** has led to slow progress of research on boiler combustion optimization and has obstacles in the *** paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization ***,this paper discusses new research challenges and outlines future research directions,which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations.

关键词： boiler combustion optimization circulating fluidized bed boiler environmental protection computational intelligence intelligent optimization algorithm

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Integrating Fiber Optic Data in Numerical Reservoir Simulation Using intelligent optimization Workflow

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SENSORS 2020年第11期20卷 3075.-3075.页

作者： Feo, Giuseppe Sharma, Jyotsna Cunningham, Stephen Louisiana State Univ Dept Petr Engn Baton Rouge LA 70803 USA Vaquero Energy Santa Maria CA 93454 USA

A novel workflow is presented for integrating fiber optic Distributed Temperature Sensor (DTS) data in numerical simulation model for the Cyclic Steam Stimulation (CSS) process, using an intelligent optimization routine that automatically learns and improves from experience. As the steam-oil relationship is the main driver for forecasting and decision-making in thermal recovery operations, knowledge of downhole steam distribution across the well over time can optimize injection and production. This study uses actual field data from a CSS operation in a heavy oil field in California, and the value of integrating DTS in the history matching process is illustrated as it allows the steam distribution to be accurately estimated along the entire length of the well. The workflow enables the simultaneous history match of water, oil, and temperature profiles, while capturing the reservoir heterogeneity and the actual physics of the injection process, and ultimately reducing the uncertainty in the predictive models. A novel stepwise grid-refinement approach coupled with an evolutionary optimization algorithm was implemented to improve computational efficiency and predictive accuracy. DTS surveillance also made it possible to detect a thermal communication event due to steam channeling in real-time, and even assess the effectiveness of the remedial workover to resolve it, demonstrating the value of continuous fiber optic monitoring.

关键词： distributed fiber optic sensing intelligent optimization algorithm numerical reservoir simulation enhanced oil recovery cyclic steam stimulation automated history match

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Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

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Chinese Physics B 2023年第2期32卷 53-59页

作者：程鑫鲁秀娟刘亚楠匡森 Department of Automation University of Science and Technology of ChinaHefei 230027China Department of Mechanical Engineering The University of Hong KongHong Kong 999077China Quantum Machines Unit Okinawa Institute of Science and Technology Graduate UniversityOkinawa 904-0495Japan

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria *** conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

关键词： quantum control state preparation intelligent optimization algorithm

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Inverting Single Crystal Elastic Constants of Polycrystals with Metallographic Measurement and Modified fi ed Ultrasonic Backscatter Mode

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MATERIALS TRANSACTIONS 2024年第9期65卷 1116-1125页

作者： Zhang, Xia Li, Xiongbing Xiangnan Univ Coll Comp & Artificial Intelligence Chenzhou 423000 Hunan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

Single crystal elastic constants are vital parameters associated with the mechanical properties of polycrystals. To accurately invert these constants, precise experimental techniques must be employed. However, traditional ultrasonic measurement methods can introduce errors when dealing with complex microstructures. To address this issue, we propose an approach that combines metallographic measurements, homogenization techniques, and intelligent algorithms to invert the single crystal elastic constants of polycrystals. First, we developed a modified fi ed single scattering response (SSR) model based on metallographic measurements and the best-fitted fi tted spatial correlation function (SCF). In addition, different ff erent homogenization techniques were compared to enhance the accuracy of the model. We then propose an Adaptive Probability Differential ff erential Evolution Quantum Particle Swarm optimization (AP-DE-QPSO) algorithm to enhance the accuracy of optimization results with an adaptive probability (AP) operator in the optimization process. After verification fi cation with three standard test functions, we demonstrated that the algorithm achieved higher accuracy. Finally, by utilizing the AP-DE-QPSO algorithm and fi tting the spatial variance of the modified fi ed SSR model to the ultrasonic backscattering experimental data, we successfully inverted the global optimal solution of the single crystal elastic constants. The Self- Consistent averaging technique yielded the best results, with relative errors of approximately 3% % for C 11 and C44, 44 , and a relative error of approximately 10% % for C12. 12 . Our study confirms fi rms the accuracy and reliability of the modified fi ed SSR model and AP-DE-QPSO algorithm in inverting the elastic constants of single crystals. [doi:10.2320/***-M2024049] / ***-M2024049]

关键词： ultrasonic backscatter measurement polycrystals single crystal elastic constants homogenization techniques intelligent optimization algorithm

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Particle Swarm-Grey Wolf Cooperation algorithm Based on Microservice Container Scheduling Problem

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IEEE ACCESS 2023年 11卷 16667-16682页

作者： Chen, Xinying Wu, Yuefeng Xiao, Siyi Dalian Jiaotong Univ Sch Comp & Commun Engn Dalian 116600 Liaoning Peoples R China

In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.

关键词： intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal

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A STOCHASTIC MODEL AND SOCIAL optimization OF A BLOCKCHAIN SYSTEM BASED ON A GENERAL LIMITED BATCH SERVICE QUEUE

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JOURNAL OF INDUSTRIAL AND MANAGEMENT optimization 2021年第4期17卷 1845-1861页

作者： Zhao, Wenjuan Jin, Shunfu Yue, Wuyi Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Konan Univ Dept Intelligence & Informat Kobe Hyogo 6588501 Japan

Blockchain is well known as a database technology supporting digital currencies such as Bitcoin, Ether and Ripple. For the purpose of maximizing the overall revenue of the blockchain system, we propose a pricing policy to impose on transactions. Regarding the mining process as a vacation, and the block-verification process as a service, we establish a type of non-exhaustive queueing model with a limited batch service and a possible zero-transaction service. By selecting the beginning instant of a block-verification process as a Markov point and using the method of a generating function, we obtain the stationary probability distribution for the number of transactions in the system at the Markov points and analyze the elapsed time for the mining cycle. Based on the model analysis results, we derive the average latency of transactions and demonstrate how the average latency of transactions changes in relation to the arrival rate of transactions. With a reward-cost structure, we construct an individual benefit function and a social benefit function. By improving the Grasshopper optimization algorithm (GOA), we search for the Nash equilibrium and the socially optimal arrival rates of transactions. Numerical results show that the Nash equilibrium arrival rate of transactions is always higher than the socially optimal arrival rate of transactions for a given mining parameter and a specific block capacity. For this, we propose a pricing policy that forces the transactions to accept the socially optimal arrival rate and maximize the overall revenue of the blockchain system, including all transactions and miners.

关键词： Blockchain limited batch service Markov chain Nash equilibrium social optimization intelligent optimization algorithm pricing policy

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Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

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SENSOR REVIEW 2022年第1期42卷 133-144页

作者： Zhang, Yunpu Xu, Gongguo Shan, Ganlin Army Engn Univ Shijiazhuang Hebei Peoples R China Northwest Inst Nucl Technol Xian Peoples R China

Purpose Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system. Design/methodology/approach First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly. Findings Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance. Originality/value In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

关键词： Sensor scheduling Multi-sensor system Low-altitude target tracking Doppler blind zone intelligent optimization algorithm

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