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Study on optimization of Interior Ballistic Performance of Cased Telescoped Ammunition Based on Improved Firefly Algorithm

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PROPELLANTS EXPLOSIVES PYROTECHNICS 2023年第2期48卷

作者： He, Xin-Jia Li, Chao Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Peoples R China

Considering that the evolution operators of common optimization algorithms such as genetic algorithm has a poor convergence speed and solution accuracy in the interior ballistic performance optimization of artillery, an improved firefly algorithm is selected for the interior ballistic performance optimization of cased telescoped ammunition (CTA). It is applied to the interior ballistic mathematical model of CTA to carry out single-objective optimization and multi-objective optimization respectively. The results show that in the single-objective optimization, the muzzle velocity obtained by using the improved firefly algorithm is larger than other commonly used optimization algorithms. Under the constraints of maximum chamber pressure and relative burnout point, the muzzle velocity can be increased by about 30 m/s. In the multi-objective optimization, three sub-objectives of muzzle velocity, ballistic efficiency and muzzle pressure are considered respectively, and the multi-objective firefly algorithm is applied to comprehensively optimize the interior ballistic performance of CTA. Then, the results are compared with the commonly used multi-objective optimization algorithms, such as non-dominated sorting genetic algorithm version II (NSGA-II) and non-dominated sorting genetic algorithm version III (NSGA-III), the solution results are a series of Pareto optimal solutions. The obtained optimization results improved the interior ballistic performance and launch safety, providing reference for the reasonable matching of charge structure parameters and the selection of optimization algorithm upon the interior ballistic performance optimization of CTA to some extent.

关键词： Cased telescoped ammunition Improved firefly algorithm multi-objective optimization problem Interior Ballistics Charge structure parameters

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A problem-Specific multi-objective Evolutionary Algorithm

A Problem-Specific Multi-objective Evolutionary Algorithm

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2017 3rd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2017)

作者： Jun-jie DONG He-cheng LI Zhi-cang WANG School of Mathematics and Statistics Qinghai Normal University School of Computer Science Qinghai Normal University

multi-objective optimization problems are a kind of problems optimizing simultaneously several conflicting objectives and keeping a balance between the diversity and the convergence of solutions. In this paper, some novel techniques are designed to improve the efficiency of multi-objective evolutionary algorithms. Firstly, a specific sub-function is separated from a series of objectives, which is applied to provide an approximate search direction and speed the convergence of the algorithm. Then, the crowding degree scheme, as in NSGA-II, is used to select potential promising solutions in the process of iterations such that Pareto solution set has more uniform and extensive distribution. Finally, a novel multi-objective evolutionary algorithm is presented by embedding these schemes intoMOEA/D. The simulation results show the proposed algorithm is feasible and efficient.

关键词： multi-objective optimization problem Evolutionary algorithm Optimal solutions problem information

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A Novel Quantum-inspired multi-objective Evolutionary Algorithm Based on Cloud Theory

A Novel Quantum-inspired Multi-Objective Evolutionary Algori...

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2011中国人工生命与智能机器人会议

作者： Bo Xu WANG CHENG Jian-Ping Yu Yong Wang Department of Computer Science and Technology Guangdong University of Petrochemical TechnologyMaom Wells Fargo Bank USA College of Mathematics and Computer Science Hunan Normal UniversityChangshaHunan410081 College of Electrical and Information Engineering Hunan UniversityChangshaHunan410082

In the previous papers, Quantum-inspired multi-objective evolutionary algorithm (QMEA) was proved to be better than conventional genetic algorithms for multi-objective optimization problem. To improve the quality of the non-dominated set as well as the diversity of population in multi-objective problems, in this paper, a Novel Cloud-based quantum-inspired multi-objective evolutionary Algorithm (CQMEA) is proposed. CQMEA is proposed by employing the concept and principles of Cloud theory. The algorithm utilizes the random orientation and stability of the cloud model, uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient. By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly, Compared with several well-known algorithms such as NSGA-II，QMEA. Experimental results show that (CQMEA) is more effective than QMEA and NSGA-II.

关键词： multi-objective optimization problem Quantum-Inspired multi-objective Evolutionary Algorithm Cloud Model Evolutionary Algorithm

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multi objective optimization of Diesel Engine Emission System Based on NSGA-II 21

Multi Objective Optimization of Diesel Engine Emission Syste...

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Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Xuemin Li Qihang Zhang Mingyao Liu Ziyang Gu Cunxi Zhu Chengjie Zheng Harbin Engineering University China

ISBN: (纸本)9781450385053

As a more efficient power machine at present, diesel engine is widely used in industry, large vehicles, ships, power generation and other industries. Because of its advantages of high thermal efficiency, low fuel consumption, strong power and long service life that diesel engine will continue to occupy a leading position in its application field in the next few decades.[1] However, because the diesel engine uses complex hydrocarbons as fuel, the air pollution caused by its exhaust gas is very serious, so the development of diesel engine in the future must shift from the concept of only power and economy to paying equal attention to both emission and economy. In this paper, the mainstream algorithm principle is introduced based on Non-dominated Sorting Genetic Algorithms – II. Aiming at solving the optimal solution between these conflicting parameters.

关键词： Diesel engine optimization multi-objective optimization problem Non-dominated Sorting Genetic Algorithms – II(NSGA-II)

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Stream Cipher Generation Method Based on Particle Gradient multiobjective Evolutionary Algorithm

Stream Cipher Generation Method Based on Particle Gradient M...

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Third International Symposium on Information Science and Engineering(第三届信息科学与工程国际会议 ISISE 2010)

作者： Liu Jianshen Yin Baoyong School of Science Jiangxi University of Science and Technology Ganzhou China

multi objective Evolutionary Algorithm (MOEA) has been used to design a high safe and high efficiency cryptosystem. Because stream cipher cryptosystem makes itself can not be decrypted theoretically, a large majority of sequence cipher cryptosystem use a short key to produce a key sequence with the same period. But the key sequence produced above seldom has good randomness and is unsafe frequently. This paper proposes an improved approach-Stream Cipher Generation Method based on Particle Gradient multiobjective Evolutionary Algorithm (PGEASCG) to produce ft key sequence with good randomness and reliability. Simulation results clearly illustrate that the proposed approach is very efficient and could easily get the optimal key sequence.

关键词： stream cipher multi-objective optimization problem particle gradient multi-objective evolutionary algorithm

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multi objective optimization of Diesel Engine Emission System Based on NSGA-II

Multi Objective Optimization of Diesel Engine Emission Syste...

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2021年第四届算法、计算和人工智能国际会议

作者： Xuemin Li Qihang Zhang Mingyao Liu Ziyang Gu Cunxi Zhu Chengjie Zheng Harbin Engineering University

关键词： multi-objective optimization problem Non-dominated Sorting Genetic Algorithms – II(NSGA-II) Diesel engine optimization

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Solving multi-Stage Weapon Target Assignment problems by C-TAEA

Solving Multi-Stage Weapon Target Assignment Problems by C-T...

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第三十九届中国控制会议

作者： Shiqi Zou Xiaoping Shi Rui Guo Xiaohan Lin Control and Simulation Center Harbin Institute of Technology

multi-stage weapon target assignment(MSWTA) problem is usually treated as a classical constrained multi-objective optimization problem, which tends to balance the attack efficiency and economic loss for weapons after adding engagement resource constraints. In the context of multi-stage confrontations, the MSWTA becomes more complex and challenging to solve. To handle this constrained problem, this paper develops the advantageous two-archive evolutionary algorithm(C-TAEA) to combine the density estimation mechanism with a fast non-dominated sorting method to obtain solutions to this problem efficiently, while maintaining the convergence-oriented archive(CA) to speed up the generation of solutions and the diversity-oriented archive(DA) to enhance the diversity of populations. Extensive experiments are conducted to compare the proposed C-TAEA with the traditional NSGA-Ⅱ according to the average of IGD and HV values. The numerical simulation results demonstrate that our method intuitively shows the performance improvement after adding the feasibility and can find the approximate Pareto Front more effectively.

关键词： Weapon Target Assignment(WTA) multi-objective optimization problem Two-Archive C-TAEA NSGA-Ⅱ Density Estimation

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Particle Gradient multi-objective Evolutionary Algorithm Based on GPU with CUD A

Particle Gradient Multi-objective Evolutionary Algorithm Bas...

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Third International Symposium on Information Science and Engineering(第三届信息科学与工程国际会议 ISISE 2010)

作者： Xuezhi Yue Zhijian Wu Kangshun Li State Key Laboratory of Software Engineering Wuhan University Wuhan China School of Science Jiangxi State Key Laboratory of Software Engineering Wuhan University Wuhan China School of Science Jiangxi University of Science of Technology Ganzhou China School of Information S

In the paper, particle gradient multi-objective evolutionary algorithm (PGMOEA) on GPU is presented. PGMOEA extends the classical particle dynamic multiobjective evolutionary algorithm by incorporating the gradient information of each particle from evolutionary programming. We perform experiments to compare PGMOEA on GPU with PGMOEA on CPU and demonstrate that PGMOEA on GPU is much more effective and efficient than PGMOEA on CPU.

关键词： multi-objective optimization problem particle gradient multi-objective evolutionary algorithm graphics processing unit compute unified device architecture

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optimization Spectrum Decision Parameters in CR using Autonomously Search Algorithm

Optimization Spectrum Decision Parameters in CR using Autono...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Yongcheng Li Hai Shen Manxi Wang State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Physics Science and Technology Shenyang Normal University Control Theory and Control Engineering Postdoctoral Research Station Shenyang Institute of Automation

In order to solve the contradiction between wireless communication service demand and spectrum resource shortage and enhance the utilization rate of spectrum, Cognitive Radio technology is necessary. Firstly, this paper presents a cognitive engine framework structure, and then the concept of bio-inspired and its application in CR computing were emphatically introduced. Finally, in order to solve spectrum parameters problem, this paper proposed based on autonomously search algorithm. Based on population evolution,ASA algorithm employs the foraging, reproduction, selection and mutation operators, and was tested under the multicarrier simulation environment. The experiment results show that ASA algorithm can better adjust each subcarrier communication parameters according to the requirement of cognitive engine parameters optimization, which include transmitted power,modulation mode, and bit-error-rate and so on, and finally satisfy the channel condition and the dynamic changes of the user service.

关键词： Cognitive Engine Bio-inspired Computing Cognitive Radio Automously Search Algorithm multi-objective optimization problem

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Nature inspired optimization tools for SVMs - NIOTS

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METHODSX 2021年 8卷 101574页

作者： da Silva Santos, Carlos Eduardo Coelho, Leandro dos Santos Llanos, Carlos Humberto Univ Brasilia UnB Brasilia DF Brazil Inst Fed Educ Ciencia & Tecnol Tocantins IFTO Palmas TO Brazil Pontif Univ Catolica Parana PUC Curitiba Parana Brazil

Support Vector Machines (SVMs) technique for achieving classifiers and regressors. However, to obtain models with high accuracy and low complexity, it is necessary to define the kernel parameters as well as the parameters of the training model, which are called hyperparameters. The challenge of defining the more suitable value to hyperparameters is called the Parameter Selection problem (PSP). However, minimizing the complexity and maximizing the generalization capacity of the SVMs are conflicting criteria. Therefore, we propose the Nature Inspired optimization Tools for SVMs (NIOTS) that offers a method to automate the search process for the best possible solution for the PSP, allowing the user to quickly obtain several sets of good solutions and choose the one most appropriate for his specific problem. The PSP has been modeled as a multiobjective optimization problem (MOP) with two objectives: (1) good precision and (2) low complexity (low number of support vectors). The user can evaluate multiple solutions included in the Pareto front, in terms of precision and low complexity of the model. Apart from the Adaptive Parameter with Mutant Tournament multiobjective Differential Evolution (APMT-MODE), the user can choose other metaheuristics and also among several kernel options. (C) 2021 Published by Elsevier B.V.

关键词： Support vectors machines Parameters selection problem Adaptive parameters control Differential evolution algorithm multi-objective optimization problem

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