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International Conference on Computational Intelligence and Natural Computing

作者： Wang, Maocai Dai, Guangming Hu, Hanping Pen, Lei Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Wuhan 430074 Peoples R China

ISBN: (纸本)9780769536453

Applying evolution algorithm to selection of security elliptic curve(EC) is first researched in the paper. In order to ensure the security of elliptic curve cryptosystem(ECC), it is necessary that the elliptic curves are safe against all kinds of attack algorithms about the elliptic curve discrete logarithm problem (ECDLP). Therefore, selection of secure elliptic curves is a mathematics difficult problem. In this paper, selection principles of secure EC are firstly analyzed. Then, based on the selection principle, a selection algorithm of security EC based on evolution algorithm is put forward. The thoughts of encoding, crossover and mutation of evolution algorithm for selection of EC are discussed in detail. The implementation result shows the availability and efficiency of the algorithm.

关键词： Elliptic curve Selection algorithm ECDLP evolution algorithm

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UAV track planning based on evolution algorithm in embe dded system

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MICROPROCESSORS AND MICROSYSTEMS 2020年 75卷 103068-103068页

作者： Niu, Xi Yuan, Xiaodong Zhou, Yongwei Fan, Hehua Zhengzhou Univ Aeronaut Sch Art Design Zhengzhou 450046 Henan Peoples R China

The embedded controller is small in size and powerful in computing power, and can quickly complete the related processing and calculation of flight attitude and track planning data. Wireless data transmission can realize a long-distance data transmission between the aircraft command center and the airborne control platform. Based on the data fusion algorithm of the Kalmanz filter, the data collected by multiple sensors can be integrated, which can effectively reduce the measurement noise amplitude. At the same time, the cumulative error of a single sensor is reduced. In order to solve the problems of ant colony algorithm in case it is easy to fall into the local extremum and the convergence speed is slow, an improved ant colony algorithm for 3D navigation of unmanned aerial vehicles is proposed for trace planning. This study divides the three-dimensional track planning into two parts based on the improved ant colony algorithm for two-dimensional plane planning and height planning. Geometric optimization methods to enhance the guidance of ant search are used. According to the distance and height constraints between the track point and the threat source, the altitude of the track points to plan the 3D track of the drone is calculated and adjusted. At the same time, the adaptive parameter adjustment method is used to improve the ant colony search ability and the interaction ability between individuals, and effectively get rid of the situation to avoid to falling into a local optimum. In addition, the index function is established and the path is smoothed. Simulation results show that the proposed improved algorithm cannot only safely avoid threats in the three-dimensional environment, but also has the ability to find the optimal solution and the convergence speed is better than the original algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Embedded system UAV Track planning evolution algorithm

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An application of augmented Lagrangian differential evolution algorithm for optimizing the speed of inland ships sailing on the Yangtze River

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INTERNATIONAL JOURNAL OF NAVAL ARCHITECTURE AND OCEAN ENGINEERING 2022年 14卷

作者： Zhang, Longhui Peng, Xiuyan Liu, Zhengfeng Wei, Naxin Wang, Fei Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China China Ship Sci Res Ctr Wuxi Jiangsu Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Sch Life Sci Hefei Anhui Peoples R China

As an effective way to achieve energy conservation and emission reduction, the speed optimization of inland ships has received increasing attention. In this study, a modeling method for the speed optimization of inland ships sailing on the Yangtze River and a heuristic algorithm called Augmented Lagrangian Differential evolution (ALDE) method are proposed. The ALDE introduces adaptive control parameters, an elite strategy, and opposition-based learning technology for optimization. To validate the performance of ALDE, it is compared with six other algorithms to solve several well-known constrained optimization problems, and the results show that the ALDE outperforms these six algorithms. To construct a speed optimization model, a discretization model for the Yangtze River waterway is established by segmenting the waterway. Considering the relationship among the environment, ship, engine, and propeller, a fuel consumption prediction model is built, and then, the objective optimization function for inland ship speed optimization is established by integrating the discrete model as well as the fuel consumption model. Finally, numerical simulation results show that the ALDE can realize less fuel consumption for an inland ship sailing from Yibin Port to Jiangyin Port, verifying the feasibility of the ALDE for speed optimization of inland ships. (c) 2022 Society of Naval Architects of Korea. Production and hosting by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Inland ships Fuel consumption Speed optimization Augmented Lagrangian differential evolution algorithm Energy conservation

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A novel traffic flow prediction model: Variable order fractional grey model based on an improved grey evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期224卷

作者： Zhang, Ruyue Mao, Shuhua Kang, Yuxiao Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China

Short-time traffic flow prediction plays an essential role in Intelligent Transportation System. Mixed flow is a common type of short-time traffic flow, with characteristics of small sample size, high temporal variability, and evident phase. To better predict the changes of mixed flow and speed, a new variable order fractional grey model (NVOFGM) is proposed by introducing the variable order fractional integro-differential (VO-FID) model into the grey model and extending the first accumulation generation to variable order fractional accumulation generation (VO-FAGO). The analytical expression of NVOFGM is obtained using Laplace transform. At the same time, considering that NVOFGM needs to optimize the parameters, and the objective function of the optimization is the nonlinear function with many parameters, a new grey prediction evolution algorithm (CDEGPEA) is obtained by improving the grey prediction evolution algorithm by the chaotic logistic map and the differential evolution algorithm (DE), and the superiority and effectiveness of its performance are validated on CEC2005 benchmark functions. Finally, based on the mixed flow and speed data from the M3 and M25 freeways in the UK, it is found that NVOFGM performs well in both fitting and testing and has good stability and stable generalization ability.

关键词： Short -time traffic flow Grey model evolution algorithm Optimization

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FMS Scheduling Simulation Based on an evolution algorithm

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International Journal of Plant Engineering and Management 2002年第3期7卷 170-178页

作者： LI De-xin LU Yan-jun JIA Jie ZHAO Hua-qun Xi'an University of Technology Xi'an 710048 P. R. China Shaanxi Movie and Television Academy Xi'an 710054 P. R. China

A FMS (flexible manufacturing system)scheduling algorithm based on an evolution algorithm (EA) is developed by intensively analyzing and researching the scheduling method in this *** factors related to FMS scheduling are considered *** explanations for a common kind of the encoding model are *** rationality of encoding model is ensured by designing a set of new encoding methods,while the simulation experiment is *** results show that a FMS scheduling optimum problem with multi-constraint conditions can be effectively solved by a FMS scheduling simulation model based on *** this method with others,this algorithm has the advantage of good stability and quick convergence.

关键词： FMS scheduling evolution algorithm object-oriented simulation optimization

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An Island-Based evolution algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2005年第4期9卷 368-371页

作者： Jedrzejowicz, Piotr Skakovski, Aleksander Gdynia Maritime Univ Chair Informat Syst Ul Morska 83F PL-81225 Gdynia Poland Gdynia Maritime Univ Comp Sci Dept PL-81225 Gdynia Poland

A problem of scheduling nonpreemtable tasks on parallel identical machines under an additional continuous resource to minimize the schedule length is considered in the paper. A continuous resource is divisible continuously and is allocated to tasks fromgiven intervals in amounts unknown in advance. Task processing rate depends on the allocated amount of the continuous resource. The considered problem can be solved in two steps. The first step involves generating all possible task schedules and second - finding an optimal schedule among all schedules with optimal continuous resource allocation. The second step can be formulated as a convex programming problem with linear constraints that can be solved by specialized solvers. To eliminate time consuming optimal continuous resource allocation a problem Theta(Z) with continuous resource discretisation is introduced. Because Theta(Z) is NP-hard an island-based evolution algorithm (IBEA) is proposed to tackle the problem. Experimental results proved IBEA to be competitive with known algorithms for solving the considered problem.

关键词： discrete-continuous scheduling evolution algorithm

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Deep Reinforcement Learning evolution algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

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FUTURE INTERNET 2023年第1期15卷 34-34页

作者： Yang, Siyuan Bouazizi, Mondher Ohtsuki, Tomoaki Shibata, Yohei Takabatake, Wataru Hoshino, Kenji Nagate, Atsushi Keio Univ Grad Sch Sci & Technol Yokohama 2238522 Japan Keio Univ Fac Sci & Technol Dept Informat & Comp Sci Yokohama 2238522 Japan SoftBank Corp Technol Res Lab Tokyo 1350064 Japan

In this paper, we propose a novel Deep Reinforcement Learning evolution algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users' throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the evolution algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

关键词： HAPS antenna control reinforcement learning evolution algorithm

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Manifold Block Optimization Method by Utilizing evolution algorithm

Manifold Block Optimization Method by Utilizing Evolution Al...

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2009中国控制与决策会议

作者： Jingben Yin,Lei Zhao,Juntao Wang Department of Mathematics,Henan Institute of Science and Technology,Xinxiang 453003,China

In this paper by utilizing evolution algorithm we proposed an optimization method for manifold block design by using visual basic 6.0 *** our designed visual basic 6.0 programs the geometric parameters of hydraulic elements are putted into special designed drawing interface procedure,so the manifold block drawing of hydraulic system can be automatically produced by our designed *** finally,the numerical results by the designed procedure implies that the computational efficiency is greatly improved and the computational time of designing is largely shortened.

关键词： optimization design manifold block design evolution algorithm

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Manifold Block Optimization Method by Utilizing evolution algorithm

Manifold Block Optimization Method by Utilizing Evolution Al...

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2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

作者： Jingben Yin Lei Zhao Juntao Wang Department of Mathematics Henan Institute of Science and Technology Xinxiang 453003 China

In this paper by utilizing evolution algorithm we proposed an optimization method for manifold block design by using visual basic 6.0 programs. In our designed visual basic 6.0 programs the geometric parameters of hydraulic elements are putted into special designed drawing interface procedure, so the manifold block drawing of hydraulic system can be automatically produced by our designed procedure. At finally, the numerical results by the designed procedure implies that the computational efficiency is greatly improved and the computational time of designing is largely shortened.

关键词： optimization design manifold block design evolution algorithm

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A Matrix Adaptation evolution Strategy Based evolution algorithm for Large-scale Many-objective Optimization

A Matrix Adaptation Evolution Strategy Based Evolution Algor...

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第32届中国控制与决策会议

作者： Ling Yang Jianchang Liu Fei Li Shubin Tan Tianzi Zheng Yuanchao Liu School of Information Science and Engineering Northeastern University Department of Electrical and Information Engineering Anhui University of Technology

In recent years,multi-objective optimization problems have been received a lot of ***,many applications involve hundreds or thousands of decision variables,which pose a serious barrier for evolutionary *** this paper,therefore,a novel algorithm,denoted as matrix adaptation evolution strategy based evolution algorithm(MAES-EA),is proposed for large-scale multi-objective *** the proposed algorithm,the decision variables are divided into convergence related variables and diversity related variables,which are optimized by different optimization *** be specific,the matrix adaptation evolution strategy is introduced to reduce the amount of evaluations when optimizing the convergence related *** addition,the L-norm-based distance is applied to optimize diversity related *** performance of the proposed algorithm is compared with some state-ofthe-art methods on LSMOP test instances,and results show that MAES-EA is suitable for large-scale multi-objective optimization.

关键词： MAES Large-scale multi-objective optimization decision variables evolution algorithm

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