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Optimization of the Critical Slip Surface of Three-Dimensional Slope by Using an improved genetic algorithm

INTERNATIONAL JOURNAL OF GEOMECHANICS 2020年第8期20卷

作者： Zhou, X. P. Huang, X. C. Zhao, X. F. Chongqing Univ State Key Lab Coal Mine Disaster Dynam & Control Chongqing 400045 Peoples R China Hunan Univ Sci & Technol Sch Civil Engn Xiangtan 411201 Peoples R China Chongqing Univ Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China

An improved genetic algorithm is proposed to optimize the potential sliding surface of slopes. Compared with a conventional genetic algorithm, the genetic recombination between the previous generation and the newly generated population around the local optimal individual of the previous generation is performed in the improved genetic algorithm, while the genetic recombination between the previous generations is performed in the conventional genetic algorithm. The improved genetic algorithm has the advantage of faster convergence than the conventional genetic algorithm. Moreover, the rigorous limit equilibrium method is applied to define the safety factor of slopes. Two examples are illustrated to prove the efficiency of the improved genetic algorithm.

关键词： improved genetic algorithm Three-dimensional slope Critical slip surface Safety factor Rigorous limit equilibrium method

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Identification of Crop Diseases Based on improved genetic algorithm and Extreme Learning Machine

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Computers, Materials & Continua 2020年第10期65卷 761-775页

作者： Linguo Li Lijuan Sun Jian Guo Shujing Li Ping Jiang School of Computer Nanjing University of Posts and TelecommunicationsNanjing210003China College of Information Engineering Fuyang Normal UniversityFuyang236041China Laboratory of Information and Computing Science The Western UniversityOntarioN6A 3K7Canada

As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of *** address the problems of low crop-disease identification precision and detection abilities,a new method of detection is proposed based on improved genetic algorithm and extreme learning *** five different typical diseases with common crops as the objects,this method first preprocesses the images of crops and selects the optimal features for ***,it builds a model of crop disease identification for extreme learning machine,introduces the hill-climbing algorithm to improve the traditional genetic algorithm,optimizes the initial weights and thresholds of the machine,and acquires the approximately optimal *** finally,a data set of crop diseases is used for verification,demonstrating that,compared with several other common machine learning methods,this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.

关键词： Crops disease identification extreme learning machine improved genetic algorithm

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Application of improved genetic algorithm in ultrasonic location of transformer partial discharge

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NEURAL COMPUTING & APPLICATIONS 2020年第6期32卷 1755-1764页

作者： Zhu, Youchan Zhou, Li Xu, Haisheng N China Elect Power Univ Sch Control Comp Engn Baoding Peoples R China

Detecting and locating the partial discharge point inside the transformer is one of the key measures to ensure the normal operation of transformer. In this paper, an improved genetic algorithm (GAICM) based on improved cross-model was proposed. In this algorithm, crossover operator and mutation operator are improved, and an adaptive model is introduced. GAICM was tested by classical test functions. The test results showed that GAICM has good convergence speed and optimization ability. Meanwhile, GAICM was applied to the ultrasonic localization of transformer partial discharge, and its positioning effect was compared with the location effect of genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm. The experimental result showed that the application of GAICM to the ultrasonic localization of the transformer partial discharge is more effective.

关键词： improved genetic algorithm Particle swarm optimization algorithm Simulated annealing algorithm Transformer partial discharge

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A divide-and-conquer based improved genetic algorithm for network selection in heterogeneous wireless network

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2020年第3期34卷 217-224页

作者： Sun, Hui Yang, Chuang Wang, Rui Ghauri, Sabir Civil Aviat Univ China Dept Automat Coll Informat Engn & Automat Tianjin 300300 Peoples R China Univ West England Dept Engn Design & Math Frenchy CampusColdharbour Lane Bristol BS16 1QY Avon England Civil Aviat Univ China Dept Elect Engn Coll Informat Engn & Automat Tianjin 300300 Peoples R China

Developing and using wireless network is a trend to serve passengers in aircraft cabins. Cognitive radio technology can be used in aircraft cabin wireless environment to solve the spectrum resource scarcity problem. Heterogeneous network access issue should be noticeable in cognitive radio wireless networks. In this paper, an improved genetic algorithm (GA) based on divide-and-conquer is proposed to handle this problem. The proposed approach is compared with original GA, particle swarm optimisation (PSO) algorithm and tabu search (TS) algorithm. The simulation results prove that the proposed method can obtain greater bandwidth with higher efficiency.

关键词： aircraft cabin wireless network heterogeneous divide-and-conquer cognitive radio network access improved genetic algorithm

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Research on improved genetic algorithm in path optimization of aviation logistics distribution center

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期38卷 29-37页

作者： Sun, Yixiang Geng, Nana Gong, Shuli Yang, Yinbao Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Jiangsu Peoples R China Jiangsu Police Inst Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Modern Post Nanjing Jiangsu Peoples R China

At present, airlines cannot meet the requirements of increasing air cargo volume and cargo service quality by simply improving air transportation capacity. On the basis of increasing the construction of aviation logistics distribution centers, airlines also need to optimize the distribution route. By analyzing and comparing the application of genetic algorithm in the problem of distribution path optimization, this paper proposes a new adaptive genetic algorithm with adaptive mutation to improve the search ability in the local range, which has faster convergence speed than the general genetic algorithm. And solve the logistics node location model. The result proves that the algorithm is faster and more accurate, and can solve the transportation path problem in distribution.

关键词： improved genetic algorithm distribution center path optimization aviation logistics

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improved genetic algorithm for parameters identification of cart-double pendulum

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JOURNAL OF VIBROENGINEERING 2019年第6期21卷 1587-1599页

作者： Dan, Yuanhong Xu, Peng Zhang, Wei Tan, Zhi Chongqing Univ Technol Chongqing Peoples R China

Cart-double pendulum is a typical nonlinear under-actuated mechanical device, and highly sensitive to modeling error and external disturbance. Motion control of double pendulum is a very complex and difficult task, especially when it turns to real-time situation because a minor difference between kinetic model and actual device could lead to the necessity of significant adjustments according to control laws comparing to the usage of simulation controller. The use of accurate kinetic model is the key factor for fast transition from simulation to real-time operation. An improved genetic algorithm (IGA), which includes orthogonal experiment design, feedback mutation, pairing operation based on Hamming distance, and variable precision crossover operation, is introduced to identify the physical parameters of a double pendulum. The improved genetic algorithm (IGA) can overcome the shortcomings of the traditional genetic algorithm (TGA), for instance, premature convergence, and get results closer to the global optimal solution. The results of experiments demonstrate the effectiveness of the proposed method.

关键词： cart-double pendulum improved genetic algorithm feedback mutation variable precision crossover

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Optimal Equivalent Consumption Minimization Strategy for Plug-In Hybrid Electric Vehicle with improved genetic algorithm

SAE INTERNATIONAL JOURNAL OF ELECTRIFIED VEHICLES

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SAE INTERNATIONAL JOURNAL OF ELECTRIFIED VEHICLES 2020年第2期9卷 143-154页

作者： Wei, Changyin Chen, Yong Sun, Xiuxiu Zhang, Yue Hebei Univ Technol Tianjin Peoples R China

The equivalent consumption minimization strategy (ECMS) is a promising energy management approach to low-fuel economy with the outstanding features of high efficiency. In this article, an optimal ECMS by improved genetic algorithm (IGA) is proposed. To this end, we improved the genetic algorithm (GA) from the coding method, initialization mode, and cross and mutation process. And based on the comprehensive energy consumption and Pontryagin's minimum principle, the equivalent factor was derived. The IGA was used to optimize the equivalent factor. To evaluate the performance of the proposed energy management strategy (EMS), the average efficiency of the engine and the motor was analyzed in an urban area, high-speed area, and the whole area. The comprehensive fuel consumption was used as the energy consumption index, and the battery capacity loss under the transient conditions was amplified to 10 years as the evaluation battery life index. The simulation results show that under the New European Driving Cycle (NEDC), the proposed strategy improves the fuel economy and battery life index by 14.64% and 36.76%, respectively, compared with the rule-based EMS.

关键词： Plug-in hybrid electric vehicles Energy management strategy improved genetic algorithm Fuel economy Battery life

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Application of improved genetic algorithm in Cruise Missile Route Planning

Application of Improved Genetic Algorithm in Cruise Missile ...

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第二十一届分布式计算及其应用国际学术研讨会

作者： Ju Zhang Yian Liu Hailing Song Jiangnan University School of AI and Computer Science JiangnanUniversity School of AI and Computer Science

To address the shortcomings of the traditional genetic algorithm in cruise missile route planning,which is prone to "premature maturation" and premature convergence to a local optimal solution,an improved genetic algorithm is proposed that introduces an adaptive operator and a variation ratio *** algorithm processes the individual fitness values in the population by ranking ratio technique,and then adopts a selection strategy co mbining elite selection and roulette algorithm to add the feasible routes with the best fitness values directly to the children at each evolution,and then roulette selects the remaining feasible routes,which improves the global optimal search performance of the algorithm in the trajectory ***,an adaptive crossover operator is used to dynamically select the crossover probability based on the individual fitness values of the ***,the two algorithms are applied to the established map model for route planning separately,and the simulation results show that the path solved by the improved genetic algorithm reduces three planning waypoints and 10.74% of the range compared with the traditional genetic algorithm,and the global search performance of the route planning process applying the improved genetic algorithm is significantly better than that of the traditional genetic algorithm.

关键词： improved genetic algorithm ranking variation ratio adaptive crossover operator raster method

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Optimization configuration of gas path sensors using a hybrid method based on tabu search artificial bee colony and improved genetic algorithm in turbofan engine

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AEROSPACE SCIENCE AND TECHNOLOGY 2021年 112卷 106642-106642页

作者： Hu Yu Sun Zhensheng Cao Lijia Zhang Yin Pan Pengfei Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China Xian Res Inst High Technol Xian 710025 Shaanxi Peoples R China Sichuan Univ Sci & Engn Zigong 643000 Peoples R China Chinese Flight Test Estab Xian 710089 Shaanxi Peoples R China

The gas turbine engine is usually working in extreme engine conditions, especially the components along the gas path are bearing the harsh environments, such as high temperature, high pressure, strong vibration and so on, which are prone to failures. The need for security and safety makes it necessary to develop an accurate and efficient monitoring and diagnostic scheme for the gas turbine components. Sensors along the gas path provide the health status information that plays an important role in the monitoring and diagnostic scheme. Owing to the harsh installation environments, the gas path sensors are so limited that it is critical to choose the optimal sensors combination to provide rich and effective status information for health monitoring and fault diagnosis. In this study, the sensitivity of gas path parameters under various gas path component faults is firstly analyzed in a turbofan engine, which verifies that diagnosis results depend sensitively on the types and orders of sensors chosen along gas path. Therefore, an optimization scheme of gas path sensors for fault diagnosis is designed and implemented using three different meta-heuristic global optimization algorithms. The genetic algorithm (GA) is firstly exploited and improved to adapt to the real problem of sensors optimization, which has been shown to be with strong convergence ability but low computation efficiency. The artificial bee colony algorithm combining tabu search (TSABC) has the characteristics of easily realization, less parameters tuning and fast searching speed, which will be seen as a more effective meta-heuristic and proved to achieve good performance of sensors optimization and fault diagnosis, however, this algorithm still exists the defect of slow convergence. Therefore, a hybrid algorithm based on TSABC and IGA (TSABCIGA) has been then presented and compared, in which the TSABC with fast searching speed is used to obtain the initial optimal population and the IGA with strong converg

关键词： Turbofan engine Fault diagnosis Sensor optimization Tabu search artificial bee colony improved genetic algorithm

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improved genetic algorithm for Solving Flexible Job Shop Scheduling Problem with Machine Deterioration Effect 7

Improved Genetic Algorithm for Solving Flexible Job Shop Sch...

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7th IEEE International Conference on Computer Science and Network Technology (ICCSNT)

作者： Lin, Yali Zhang, Peng Dalian Jiaotong Univ Coll Software Dalian Peoples R China Dalian Jiaotong Univ Innovat Entrepreneurship Inst Educ Dalian Peoples R China

ISBN: (纸本)9781728132990

An improved cloud adaptive genetic annealing algorithm is proposed for the multi-objective FJSP with machine deterioration effect [16]. In terms of shortcomings for poor local search ability and premature convergence in GA, we improve fitness calculations, cross-variation, etc. Fitness calculation is combined with local search ability and probability jump property of simulated annealing algorithm to make it jump out of the local optimal solution. The Hamming similarity is inserted in the crossover operation, and the similarity is used to detect whether crossover operation is required, which can accelerate the running efficiency and convergence speed of the algorithm. Then, the cross-operation combines the adaptive crossover probability of the cloud model to enhance the global search capability of the algorithm. At last, we set standard position and cross position to improve cross-operation, which can enhance the global search ability of the algorithm. Through simulation experiments, the effectiveness of the algorithm for the integrated multi-objective shop scheduling algorithm is verified.

关键词： Flexible job shop scheduling improved genetic algorithm Could model Machine deterioration algorithm Cloud model Machine deterioration effect Hamming similarity

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