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China International Conference on Electricity Distribution (CICED)

作者： Liu, Chao Chen, Tanggong Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300130 Peoples R China

ISBN: (纸本)9781538667750

In order to solve the problem of multi-objective reactive power optimization, firstly the defects of current algorithms are analyzed, then an improved chaos artificial searching swarm algorithm based on artificial search is proposed. The optimization performance of the algorithm is improved by introducing chaos theory and the dynamic improvement of algorithm step. This algorithm was tested with classical test functions. The results show that the optimization ability of the artificial search swarm algorithm is significantly improved. In the MATLAB 2014b environment, ICASSA is used to optimize the simulation of the IEEE14 and IEEE30 buses of the standard power system. Comparing the performance of ICASSA, IPSO and IDE, the results show the ICASSA has better search ability. Finally, the reactive power optimization of a real power network is simulated, and the results show that the ICASSA algorithm has potential application value.

关键词： multi-objective reactive power optimization artificial searching swarm algorithm MATLAB power systems

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Multi-Objective Optimization Design of Vacuum Interrupter Structure Based on Improved artificial Jellyfish Search algorithm

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2023年第11期18卷 1806-1814页

作者： Dai, Lingrui Tiangong Univ Sch Arts Tianjin 300387 Peoples R China

An improved multi-objective optimization method based on artificial jellyfish search algorithm is proposed to solve the multivariable and multi-objective optimization problem of vacuum interrupter with mutual coupling of structural parameters and mutual restriction of performance indexes. The finite element model of 40.5 kV/31.5kA vacuum interrupter is established. The structural parameters of seven important parts such as contact diameter and contact sheet thickness in vacuum interrupter are taken as optimization variables. The optimization objective is to maximize the longitudinal magnetic field strength and minimize the conductor resistance at the peak breaking short-circuit current of vacuum interrupter. The artificial jellyfish search algorithm is improved on the population position initialization strategy and the population mutation cross-update strategy. The Pareto front solution of the structural parameters of the vacuum interrupter is obtained by the joint simulation of finite element analysis and numerical calculation software. Finally, the multi-parameter and multi-objective optimization design of vacuum interrupter structure is realized. The comprehensive performance index is introduced into the optimal parameter group of jellyfish on the Pareto front. Under the constraint of weight variables, the optimal structural parameters of contact system are obtained. The axial magnetic induction intensity of the optimized vacuum interrupter contact is increased by 13.2%, and the conductor resistance is reduced by 25.4%. The comprehensive performance of vacuum interrupter is improved by 17.8%, and the stability and reliability of the power system are further improved. (C) 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

关键词： high-voltage vacuum interrupters finite element numerical calculation artificial searching swarm algorithm multiple target optimization design

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On the Farther Analysis of Performance of the artificial searching swarm algorithm 1

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1st International Conference on swarm Intelligence

作者： Chen, Tanggong Zhang, Lijie Pang, Lingling Hebei Univ Technol Prov Minist Joint Key Lab Electromagnet Field & E Tianjin Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

artificial searching swarm algorithm (ASSA) is an intelligent optimization algorithm, and its performance has been analyzed and compared with some famous algorithms. For farther understanding the running principle of ASSA, this work discusses the functions of three behavior rules which decide the moves of searching swarm. Some typical functions are selected to do the simulation tests. The function simulation tests showed that the three behavior rules are indispensability and endow the ASSA with powerful global optimization ability together.

关键词： artificial searching swarm algorithm bionic intelligent optimization algorithm optimization evolutionary computation swarm intelligence

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artificial searching swarm algorithm for Solving Constrained Optimization Problems

Artificial Searching Swarm Algorithm for Solving Constrained...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Chen, Tanggong Pang, Lingling Du, Jiang Liu, Zibin Zhang, Lijie Hebei Univ Technol Prov Minist Joint Key Lab Electromagnet Field & E Tianjin Peoples R China

ISBN: (纸本)9781424447541

artificial searching swarm algorithm (ASSA) is a novel optimization algorithm This paper presents the comparison results on the performance of the ASSA for solving constrained optimization problems The penalty function method and non-parameter penalty method are applied to a set of constrained problem The simulation results show that ASSA is an efficient algorithm for constrained optimization problems

关键词： artificial searching swarm algorithm bionic intellident optimization algorithm optimization constrained optimization problem evolutionary algorithms

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artificial searching swarm algorithm for Solving Constrained Optimization Problems

Artificial Searching Swarm Algorithm for Solving Constrained...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Tanggong Chen,Lingling Pang,Jiang Du,Zibin Liu and Lijie Zhang Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin,China

artificial searching swarm algorithm(ASSA) is a novel optimization *** paper presents the comparison results on the performance of the ASSA for solving constrained optimization *** penalty function method and non-parameter penalty method are applied to a set of constrained *** simulation results show that ASSA is an efficient algorithm for constrained optimization problems.

关键词： artificial searching swarm algorithm bionic intellident optimization algorithm optimization constrained optimization problem evolutionary algorithms

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