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Operation Optimization of Circulating Cooling Water System Based on adaptive differential evolution algorithm

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 22-22页

作者： Niu, Dapeng Liu, Xudong Tong, Yudong Northeastern Univ Coll Informat Sci & Engn 3-11 Wenhua Rd Shenyang Peoples R China

The circulating cooling water system (CCWS) is a commonly used auxiliary system in industrial production, and it is also one of the main energy-consuming systems. The operating conditions of the system vary with the temperature changes caused by seasons, day and night, causing different energy consumption. If the system still maintains conditions when the actual conditions change, energy would be wasted. Operation optimization for the system can improve the operation effect of the system, reduce energy consumption, and increase operating efficiency. Therefore, through operation mechanism analysis for the main energy-consuming components of the CCWS that has been put into use, an operation optimization model is established integrating the domain knowledge. After that, an adaptive differential evolution (ADE) algorithm is proposed by introducing the adaptive mutation and crossover operator to solve the model. According to the minimum circulating water volume and air volume required by the system under different working conditions, the optimal start-up combination mode and operating frequency of the water pumps and fans are determined. And then, the optimal opening values of the regulating valves and baffles in the system are determined, thereby saving the energy of the system. Considering system operation knowledge, the synchronous optimization of the operation mode and operation status of the components in the system is realized. Finally, the effectiveness of the optimization method is verified through a case study.

关键词： Circulating cooling water system Operating conditions Operation optimization Domain knowledge adaptive differential evolution algorithm

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esign and Application of Optimization algorithm for Vegetable Planting and Distribution Based on adaptive differential evolution algorithm

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Procedia Computer Science 2022年 208卷 247-254页

作者： Yunjian Dai Xiaodong Li

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application.

关键词： adaptive differential evolution algorithm planting distribution of vegetables optimization algorithm internet plus

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English teaching resource database retrieval based on adaptive differential evolution algorithm

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2024年第4期34卷 380页

作者： Liu, Mengzhang Xinyang Vocat & Tech Coll Sch Appl Foreign Languages Xinyang 464000 Peoples R China

In order to improve the retrieval recall rate of English teaching resource database, the retrieval accuracy of English teaching resource database needs improvement and the retrieval time of English teaching resource database needs to be shortened. Therefore, English teaching resource database retrieval method based on adaptive differential evolution algorithm is studied. Firstly, the semantic feature distribution of English teaching resource database is analysed by combining semantic information fusion and cluster analysis. Then, the spatial undersampling technology is used to process the distributed sampling and information fusion of the English teaching resource database. Finally, according to the fusion results, the adaptive differential evolution algorithm is used to retrieve the English teaching resource database. The experimental results show that the average recall rate of the proposed method is 90.7%, the retrieval accuracy of English teaching resource database is 97.7%, and the retrieval time is 22.7 ms.

关键词： adaptive differential evolution algorithm cluster analysis semantic information fusion English teaching resource database retrieval

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The Intelligent Search Method of Ambiguity Function Main Ridge Based on the adaptive differential evolution algorithm

The Intelligent Search Method of Ambiguity Function Main Rid...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yunwei Pu Lanyu Ma Wentai Hou Yuanpu Guo Computer Center Kunming Univ. of Science and Tech. Kunming China Faculty of Information Engineering and Automation Kunming Univ. of Science and Tech.

ISBN: (纸本)9781538653746

The ambiguity function is an effective tool to solve the problem of radar emitter signals sorting in complex systems. To improve the real-time and accuracy of the ambiguity function main ridge (AFMR), this paper proposes an intelligent method to search AFMR based on adaptive differential evolution algorithm. The signal is pre-identified by the instantaneous frequency (IF) features to reduce the AFMR search range and the complexity of the problem. In this way, the differential evolution algorithm parameters can be adaptively adjusted according to the pre-recognition results to accelerate the convergence of the algorithm. Then the statistical hypothesis testing theory is used to analyze and test the search results. The experiment results show that the proposed method reduces the time of searching AFMR to about 4% compared with the exhaustive method with higher accuracy and stability.

关键词： Radar emitter signal adaptive differential evolution algorithm Ambiguity function main ridge Signal sorting and recognition differential evolution algorithms Antiferromagnetic resonance ambiguity function evolutionary algorithm ambiguity Intelligent retrieval instantaneous frequency ridges Crest

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A differential evolution algorithm with adaptive Population Size Reduction Strategy 1

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7th International Symposium on Artificial Intelligence and Robotics (ISAIR)

作者： Zhang, Xiaoyan Duan, Zhengyu Liu, Qianqian Xian Univ Sci & Technol Xian 710000 Peoples R China

ISBN: (数字)9789811979439

ISBN: (纸本)9789811979422;9789811979439

Aiming at the problem that the differential evolution algorithm easily falls into a local optimum and results in premature convergence, a new differential evolution algorithm with an adaptive population size reduction strategy (APRDE) is proposed. Firstly, in the mutation and crossover operation, to balance the local exploitation and global exploration capabilities of the algorithm, a parameter adaptive tunning scheme based on the hyperbolic tangent function and Cauchy distribution is proposed to adaptively adjust the parameter factors. Secondly, an ordered mutation strategy is adopted to guide the direction of mutating and enrich the diversity of the population. Lastly, after each evolution iteration, adaptively reducing the population size according to the error between the fitness values of individuals and the current optimal. The proposed algorithm is compared with 5 other optimization algorithms on 8 typical benchmark functions. The results show that the algorithm has a great improvement in solution accuracy, stability and convergence speed.

关键词： adaptive differential evolution algorithm Parameters tunning scheme Ordered mutation strategy Population size reduction strategy

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An enhanced distributed differential evolution algorithm for portfolio optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Song, Yingjie Zhao, Gaoyang Zhang, Bin Chen, Huayue Deng, Wuquan Deng, Wu Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Stat Yantai 264005 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Chongqing Univ Cent Hosp Dept Endocrinol Key Lab Biorheol Sci & TechnolMinist Educ Chongqing 400014 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

The population structure of differential evolution (DE) algorithm cannot maintain the diversity of the population to the greatest extent and help the population avoid to fall into the local optima in time. In this paper, a co-evolutionary multi-swarm adaptive differential evolution algorithm, namely ECMADE is proposed to solve the premature convergence and search stagnation. First of all, in terms of population structure, based on the parallel distributed framework, ECMADE randomly and evenly divides the population into exploration subpopulation, development subpopulation, and auxiliary subpopulation, and introduces an adaptive information exchange mechanism so that subpopulations can escape local optima in time. Then, a multi-operator parallel search strategy is proposed to keep population diversity and meet the optimization needs of different problems. Finally, an adaptive adjustment mechanism of control parameters is developed, through recent elite parameter archive and weight distribution to fully mine successful parameter information, and generate control parameters with a high success rate for the current evolutionary stage. In order to prove the effectiveness of the ECMADE, 10 test functions and portfolio optimization problem are selected in here. The experiment results show that the ECMADE can effectively solve these test functions, the accuracy and efficiency is superior to those of two classical DE algorithms. The actual application results show that the ECMADE can significantly improve the ability of portfolio to resist extreme losses, which proves the effectiveness and feasibility of the ECMADE once again. The ECMADE has better optimization performance by comparing with some well-known algorithms in term of the solution quality, robustness and space distribution. It provides a new algorithm for solving complex optimization problems.

关键词： adaptive differential evolution algorithm Multi-population parallel distribution structure Parallel search Portfolio optimization Fuzzy c-means clustering algorithm

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No-reference Path Receding Horizon Control for Multi-UAV Formation Reconfiguration Based on adaptive differential evolution

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第8期22卷 2633-2643页

作者： Liu, Xin Chen, Yong Zhang, Siweihua Fu, Pengcheng Xiangtan Univ Yanggutang St Xiangtan 411105 Hunan Peoples R China

As unmanned aerial vehicles (UAVs) have limited energy resources and diverse constraints, the reconfiguration of their formation encounters substantial challenges. In this paper, we employ a leader-follower method. In order to minimize flight distance and resource consumption, a greedy algorithm is used to allocate leader and follower positions. Based on the limitations of the receding horizon control (RHC) method and the control parameterization and time discretization (CPTD) method, we propose the no reference path RHC (NRPRHC) method. The proposed method transforms the formation reconfiguration into smaller local optimization problems, leading to a reduction in the size of the optimization stages and computational complexity. For each local optimization problem, we propose the adaptive population differential evolution (APDE) algorithm to optimize the control inputs. Finally, the results are provided to illustrate the feasibility and effectiveness of the proposed method.

关键词： adaptive differential evolution algorithm formation reconfiguration receding horizon control unmanned aerial vehicle

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Active Disturbance Rejection Control Based on adaptive differential evolution for Two-Wheeled Self-Balancing Robot 28

Active Disturbance Rejection Control Based on Adaptive Diffe...

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28th Chinese Control and Decision Conference

作者： Jiang, Libiao Qiu, Huachuan Wu, Zhongwei He, Jiashou South China Univ Technol Coll Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9781467397148

An active disturbance rejection control (ADRC) technique based on adaptive differential evolution (ADE) algorithm and an improved PID control strategy were proposed so as to solve the problem of self-balancing control and steering control for two-wheeled self-balancing robot. By using Lagrange formula, the nonholonomic dynamic mathematical model was derived based on the nonholonomic dynamic Routh equation of generalized coordinates. For self-balancing control, an ADRC strategy whose parameters were adjusted by means of ADE algorithm was designed. For steering control, a PID control strategy combined with tracking differentiator (TD) was designed. The dynamic model of the whole robot was built through Adams software by applying virtual prototype technology. Finally, co-simulations using both Matlab/Simulink and Adams were carried on. The co-simulation results showed that the supposed control strategy could achieve self-balancing and steering operation effectively with the performance of fast adjusting speed, high accuracy and strong robustness as well.

关键词： Two-wheeled Self-balancing Robot Mathematical Modeling Active Disturbance Rejection Control adaptive differential evolution algorithm Co-simulation

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Modeling, identification and compensation of hysteresis nonlinearity for a piezoelectric nano-manipulator

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2017年第7期28卷 907-922页

作者： Zhang, Yangming Yan, Peng Beihang Univ Beijing Peoples R China

Hysteresis nonlinearity widely exists in piezoelectric actuated nano-positioning applications, which degrades their tracking accuracy and limits their precision positioning applications. This paper presents a novel hysteresis modeling and compensation approach to alleviate the adverse effect of the asymmetric and rate-dependent hysteresis nonlinearity for a piezoelectric transducer actuated servo stage. By integrating a generalized input function with the play operator of the classical Prandtl-Ishlinskii model, a novel polynomial-based rate-dependent Prandtl-Ishlinskii (PRPI) model is proposed to capture the hysteresis behavior of the piezoelectric positioning stage, where a polynomial function of input and a time rate function of input are introduced to formulate the generalized input function. Meanwhile, a new adaptive differential evolution optimization algorithm is developed to identify the parameters of the proposed PRPI hysteresis model. Based on the PRPI hysteresis model with the identified parameters, an inverse feedforward controller is constructed to achieve the accurate tracking motion. Furthermore, the hysteresis compensation error of the proposed PRPI model is theoretically analyzed. Finally, comparative experiments are conducted, and the experimental results provided in this paper demonstrate the effectiveness and superiority of the proposed inverse PRPI model compensation approach.

关键词： Hysteresis modeling Prandtl-Ishlinskii model adaptive differential evolution algorithm piezoceramic actuator inverse hysteresis compensation

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Optimal allocation of TCSCs by adaptive DE algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第15期10卷 3844-3854页

作者： Sakr, Walla S. El-Sehiemy, Ragab A. Azmy, Ahmed M. Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafrelsheikh Egypt Tanta Univ Elect Power & Machines Engn Dept Fac Engn Tanta Egypt

This study presents an adaptive differential evolution (DE) algorithm to allocate the thyristor-controlled series compensator (TCSC) incorporated with the reactive power management problem. In addition, a dynamic updating is applied for scaling and penalty factors to enhance the performance of the multi-objective DE algorithm. During the selection of the best candidate lines for efficient allocation of TCSC devices, three situations are considered for excluded transmission lines. These situations are the lines connected between two generating units;lines connected through transformers and the lines of low losses. The considered multi-objective function compromises between reducing power losses, improving voltage profile, reducing reactive power losses, reducing TCSC cost and reducing number of flexible alternating current transmission systems units. The optimal siting and sizing of TCSC is defined for normal and emergency operating conditions. The proposed procedure is employed for IEEE 9-bus and IEEE 30-bus test systems in addition to western delta network as a real part of the Egyptian network. Four case studies are considered for each test system to represent normal and abnormal operating conditions. Results clearly indicate the effectiveness of the adaptive DE algorithm to allocate perfectly the TCSC to enhance the system performance.

关键词： evolutionary computation static VAr compensators thyristor applications power transmission lines flexible AC transmission systems power transformers reactive power power system management TCSC optimal allocation adaptive DE algorithm adaptive differential evolution algorithm thyristor-controlled series compensator allocation reactive power management problem penalty factors multiobjective DE algorithm transmission lines generating units transformers voltage profile reactive power losses reduction TCSC cost reduction flexible alternating current transmission systems units optimal siting optimal sizing emergency operating conditions IEEE 30-bus test systems IEEE 9-bus test systems Western delta network

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