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Transactions of Nanjing University of Aeronautics and Astronautics 2016年第1期33卷 1-8页

作者： Wang Long Wang Tongguang Wu Jianghai Ke Shitang Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design Nanjing University of Aeronautics and Astronautics

A novel multi-objective optimization algorithm incorporating vector method and evolution strategies,referred as vector dominant multi-objective evolutionary algorithm(VD-MOEA),is developed and applied to the aerodynamic-structural integrative design of wind turbine blades.A set of virtual vectors are elaborately constructed,guiding population to fast move forward to the Pareto optimal front and dominating the distribution uniformity with high efficiency.In comparison to conventional evolution algorithms,VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation when handling complex problems of multi-variables,multi-objectives and multi-constraints.As an example,a 1.5 MW wind turbine blade is subsequently designed taking the maximum annual energy production,the minimum blade mass,and the minimum blade root thrust as the optimization objectives.The results show that the Pareto optimal set can be obtained in one single simulation run and that the obtained solutions in the optimal set are distributed quite uniformly,maximally maintaining the population diversity.The efficiency of VD-MOEA has been elevated by two orders of magnitude compared with the classical NSGA-II.This provides a reliable high-performance optimization approach for the aerodynamic-structural integrative design of wind turbine blade.

关键词： wind turbine multi-objective optimization vector method evolution algorithm

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Cloud Based Software Computing as a Service in Hybrid evolution algorithm with Feedback Assistance

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Journal of Electronic Science and Technology 2016年第4期14卷 362-369页

作者： Ming-Shen Jian Jun-Hong Shen Kuan-Wei Lee Yi-Chen Jhou Chien-Tang Mai the Department o Computer Science and Information Engineering National Formosa University the Department of Information Communication Asia University

This paper develops cloud based software computing as a service(SCaa S) in the hybrid evolution algorithm with feedback assistance to solve the data optimal NP-complete problems such as travel salesman problems and job shop scheduling problems. Suitable steps,methods,or procedures of the genetic algorithm can be adopted from various evolution procedures or methods of the genetic algorithms based on the fitness evaluation results and survival ratio of different crossover methods in the current generation. The proposed system can dynamically emphasize the corresponding methods or procedures for the better performance in optimal solution searching. In addition,according to the proposed XML format,system users can upload only the coding of chromosomes without implementing the genetic algorithm program. Furthermore,by using the feedback assistance,the convergence time of the optimal solution can be enhanced. Index TermsCloud computing,evolution algorithm,job shop scheduling problem,traveling salesman problem.

关键词： Cloud computing evolution algorithm job shop scheduling problem traveling salesman problem

Optimal and Robust Power System Stabilizers in a Multi Machine System

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Computers, Materials & Continua 2022年第11期73卷 4139-4156页

作者： Mehrdad Ahmadi Kamarposhti Hassan Shokouhandeh Ilhami Colak Kei Eguchi Department of Electrical Engineering Jouybar BranchIslamic Azad UniversityJouybarIran Department of Electrical Engineering Semnan UniversitySemnanIran Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitecturesNisantasi UniversityIstanbulTurkey Department of Information Electronics Fukuoka Institute of TechnologyFukuokaJapan

One method for eliminating oscillations in power systems is using stabilizers.By applying an appropriate control signal in the excitation system of a generator,a power system stabilizer improves the dynamic stability of power systems.However,the issue that is of high importance is the correct design of these stabilizers.These stabilizers must be designed to have proper performance when operating conditions change.When designed incorrectly,not only they do not improve the stability margin,but also increase the oscillations.In this paper,the robust design of power system stabilizers on a four-machine power system has been performed.For this purpose,the differential evolution algorithm has been used.The studies have been repeated as three scenarios by producing different loading conditions for the generators.The performances of the conventional stabilizer and the optimized one have been compared.Simulation results indicate that the designed robust stabilizer outperforms other stabilizers in different conditions and has damped the interarea and intra-area oscillation modes in the shortest time with minimum amplitude.

关键词： Power system dynamic stability power system stabilizers robust design evolution algorithm

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

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A Matrix Adaptation Evolution Strategy Based Evolution Algor...

Manifold Block Optimization Method by Utilizing evolution algorithm

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 attention.However,many applications involve hundreds or thousands of decision variables,which pose a serious barrier for evolutionary algorithms.In this paper,therefore,a novel algorithm,denoted as matrix adaptation evolution strategy based evolution algorithm(MAES-EA),is proposed for large-scale multi-objective optimization.In the proposed algorithm,the decision variables are divided into convergence related variables and diversity related variables,which are optimized by different optimization strategies.To be specific,the matrix adaptation evolution strategy is introduced to reduce the amount of evaluations when optimizing the convergence related variables.In addition,the L-norm-based distance is applied to optimize diversity related variables.The 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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Manifold Block Optimization Method by Utilizing Evolution Al...

Feature based problem hardness understanding for requirements engineering

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

＜正＞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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Science China(Information Sciences) 2017年第3期60卷 88-107页

作者： Zhilei REN He JIANG Jifeng XUAN Shuwei ZHANG Zhongxuan LUO Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology School of Software Dalian University of Technology State Key Laboratory of Software Engineering Wuhan University

Heuristics and metaheuristics have achieved great accomplishments in various fields, and the investigation of the relationship between these algorithms and the problem hardness has been a hot topic in the research field. Related research work has contributed much to the understanding of the underlying mechanisms of the algorithms for problem solving. However, most existing studies consider traditional combinatorial problems as their case studies. In this study, taking the Next Release Problem(NRP) from the requirements engineering as a case study, we investigate the relationship between software engineering problem instances and heuristics. We employ an evolutionary algorithm to evolve NRP instances, which are uniquely hard or easy for the target heuristic(Greedy Randomized Adaptive Search Procedure and Randomized Hill Climbing in this paper). Then, we use a feature-based method to estimate the hardness of the evolved instances, with respect to the target heuristic. Experimental results demonstrate that, evolutionary algorithm can be used to evolve NRP instances that are uniquely hard or easy to solve. Moreover, the features enable the estimation of the target heuristics’ performance.

关键词： problem hardness next release problem computational intelligence requirements engineering evolution algorithm

Learning Gait of Quadruped Robot without Prior Knowledge of the Environment

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Chinese Journal of Mechanical Engineering 2012年第5期25卷 1068-1074页

作者： XU Tao CHEN Qijun College of Electronic and Information Engineering Tongji UniversityShanghai 201804China

Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most of the walking learning methods are based on robot vision system or some external sensing equipment to estimate the walking performance of certain walking parameters, and therefore are usually only applicable under laboratory condition, where environment can be pre-defined. Inspired by the rhythmic swing movement during walking of legged animals and the behavior of their adjusting their walking gait on different walking surfaces, a concept of walking rhythmic pattern（WRP） is proposed to evaluate the walking specialty of legged robot, which is just based on the walking dynamics of the robot. Based on the onboard acceleration sensor data, a method to calculate WRP using power spectrum in frequency domain and diverse smooth filters is also presented. Since the evaluation of WRP is only based on the walking dynamics data of the robot＇s body, the proposed method doesn＇t require prior knowledge of environment and thus can be applied in unknown environment. A gait learning approach of legged robots based on WRP and evolution algorithm（EA） is introduced. By using the proposed approach, a quadruped robot can learn its locomotion by its onboard sensing in an unknown environment, where the robot has no prior knowledge about this place. The experimental result proves proportional relationship exits between WRP match score and walking performance of legged robot, which can be used to evaluate the walking performance in walking optimization under unknown environment.

关键词： quadruped locomotion gait learning evolution algorithm

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An improved peak side lobe reduction method for linear array antenna for military applications

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International Journal of Intelligent Computing and Cybernetics 2021年第2期14卷 185-197页

作者： Ravi Tej D Sri Kavya Ch K Sarat K.Kotamraju Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationGunturIndia

Purpose-The purpose of this paper is to improve energy efficiency and further reduction of side lobe level the algorithm proposed is firework algorithm.In this paper,roused by the eminent swarm conduct of firecrackers,a novel multitude insight calculation called fireworks algorithm(FA)is proposed for work enhancement.The FA is introduced and actualized by mimicking the blast procedure of firecrackers.In the FA,two blast(search)forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned.To approve the presentation of the proposed FA,correlation tests were led on nine benchmark test capacities among the FA,the standard PSO(SPSO)and the clonal PSO(CPSO).Design/methodology/approach-The antenna arrays are used to improve the capacity and spectral efficiency of wireless communication system.The latest communication systems use the antenna array technology to improve the spectral efficiency,fill rate and the energy efficiency of the communication system can be enhanced.One of the most important properties of antenna array is beam pattern.A directional main lobe with low side lobe level(SLL)of the beam pattern will reduce the interference and enhance the quality of communication.The classical methods for reducing the side lobe level are differential evolution algorithm and PSO algorithm.In this paper,roused by the eminent swarm conduct of firecrackers,a novel multitude insight calculation called fireworks algorithm(FA)is proposed for work enhancement.The FA is introduced and actualized by mimicking the blast procedure of firecrackers.In the FA,two blast(search)forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned.To approve the presentation of the proposed FA,correlation tests were led on nine benchmark test capacities among the FA,the standard PSO(SPSO)and the clonal PSO(CPSO).It is demonstrated that the FA plainly beats the SPSO and the CPSO in both enhancement exactness and combination speed.The results convey that the side lobe

关键词： Antenna arrays Spectral efficiency Fill rate Interference evolution algorithm Side lobe level Paper type Research paper

Parameter Optimization for Main Driving Speed Control System of Temper Mill

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Parameter Optimization for Main Driving Speed Control System...