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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Hu, Yerong Zhang, Yong Liu, Guanjun Yang, Peng Qiu, Jing NUDT Sci & Technol Integrated Logist Support Lab Changsha Hunan Peoples R China

ISBN: (纸本)9781538653807

Testing technology is significant for improving the usability and security of a system. The test point is an important source of obtaining test information. The test layout of the system is usually based on the concept of test point. In this paper, the defects of the current test point concept to solve the problem of fault isolation in the system are analyzed, then the concept of the test path is proposed and the special point of the test layout using the test path is expounded. Then, according to the concept of test path, a bidirectional breadth first search algorithm is proposed, which completes the node search of the test path that can be detected by the specific test path, and the correlation matrix is built based on the node set. According to the problem of the current optimization selection algorithm based on the correlation matrix, the traditional artificial fish swarm algorithm is improved based the optimization model of the test path to propose a binary artificial fish swarm algorithm to optimize the test path. Finally, the effectiveness of the binary fish swarm algorithm is verified by the case.

关键词： test optimization selection testability test path bidirectional breadth first search swarm intelligence algorithm

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A New Neighborhood Topology for QUAntum Particle swarm Optimization (QUAPSO) 19

A New Neighborhood Topology for QUAntum Particle Swarm Optim...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick LiSSi EA 3956 Vitry Sur Seine France

ISBN: (纸本)9781450367486

swarm intelligence (SI) is a behavior, used first by Beni and Wang, corresponding to a system working with single and self-organized agents, interacting the ones with each other. This operating concept is implemented in many algorithms. Developed by Kennedy, Eberhart and Shi, Particle swarm Optimization (PSO) is one of them. Its behavior is based on the movements of birds swarm, and its effectiveness, for looking for the optimal solution of a given problem, is well established. Nevertheless, PSO is known for its weakness in local search. Moreover, the behavior of PSO strongly depends on internal parameters settings. In order to address these problems, we propose a new type of self-adaptive Quantum PSO (QPSO), called QUAntum Particle swarm Optimization (QUAPSO), based on quantum superposition to set the velocity parameters and hybridized with a Kangaroo algorithm in order to optimize its efficiency in local search. QUAPSO was compared with five known algorithms from the literature (classical PSO, Kangaroo algorithm, Simulated Annealing Particle swarm Optimization, Bat algorithm and Simulated Annealing Gaussian Bat algorithm) on a set of 19 test functions. The results show that QUAPSO outperforms the competing algorithms.

关键词： Particle swarm Optimization swarm intelligence algorithm Auto-adaptive algorithm Self-organization

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Improved Particle swarm Optimization algorithm with Fireworks Search 41

Improved Particle Swarm Optimization Algorithm with Firework...

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第41届中国控制会议

作者： Xiang Jinwei Jiang Chengpeng Cheng Zhizhao Xiao Wendong School of Automation and Electrical Engineering University of Science and Technology Beijing Shunde Graduate School University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Particle swarm Optimization(PSO) is an iterative swarm intelligence algorithm,whose main idea is introducing random factors into the evolutionary equation to simulate the foraging behavior of ***,this random search model gives rise to the weakened local search ability of PSO,the premature convergence problem and the inability to find the optimal *** improve this issue,an Improved Particle swarm Optimization with Fireworks Search(FS-PSO) is proposed in this *** FS-PSO,explosive sparks and gaussian mutation sparks are used to search at the end of the iteration of PSO based on the search mechanism of the Fireworks algorithm(FWA),which is aiming to increase the population diversity of the particles and enhance the local search ability of *** simulation results show that the improvement of FS-PSO is effective to the premature convergence problem of PSO.

关键词： Particle swarm Optimization Premature Convergence Problem Fireworks Search swarm intelligence algorithm

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Animal Group Behavioral Model with Evasion Mechanism

Animal Group Behavioral Model with Evasion Mechanism

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International Joint Conference on Neural Networks (IJCNN)

作者： Duan, Zhiping Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai 200433 Peoples R China

ISBN: (纸本)9781479914845

Modeling behavioral mechanism of animal group promotes the development of group animation and other fields involving crowd simulation. This paper introduces a model to mimic behaviors of animal group. We proposes a swarming intelligence algorithm, Evasion Mechanism Artificial Fish School algorithm (EM-AFSA) in our model, in which AFSA often focuses on optimization. The EM-AFSA introduces a new mechanism, i.e. evasion, which enables the group to avoid obstacles and collisions and to evade predation. It also includes flocking, foraging and tailgating. It is convenient to show the dynamic demonstration of our model and the model vividly mimics the real animal group behavior, which could potentially be used in designing group animation.

关键词： EM-AFSA swarm intelligence algorithm group animation group behavior evasion mechanism

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An efficient high-dimensional gene selection approach based on the Binary Horse Herd Optimization algorithm for biologicaldata classification

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Iran Journal of Computer Science 2024年第2期7卷 279-309页

作者： Mehrabi, Niloufar Haeri Boroujeni, Sayed Pedram Pashaei, Elnaz School of Computing Clemson University Clemson SC United States Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis United States

The Horse Herd Optimization algorithm (HOA) is a new meta-heuristic algorithm inspired by the behaviors of horses of different ages. It was recently introduced to solve complex and high-dimensional problems. This paper proposes a binary version of the HOA, termed the Binary Horse Herd Optimization algorithm (BHOA), designed to solve discrete problems and select prominent feature subsets. Additionally, this study introduces a novel hybrid feature selection framework that combines the BHOA with a Minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid approach, more computationally efficient, yields a beneficial subset of relevant and informative features. Recognizing feature selection as a binary problem, we have applied a new Transfer Function (TF), named the X-shape TF, which converts continuous problems into binary search spaces. Moreover, the Support Vector Machine (SVM) is employed to evaluate the efficiency of the proposed method on ten microarray datasets: Lymphoma, Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. Compared to other state-of-the-art methods, such as the Gray Wolf (GW), Particle swarm Optimization (PSO), and Genetic algorithm (GA), our proposed hybrid method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and minimal selected features. Experimental results also show that the X-shaped BHOA approach outperforms other methods. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Feature selection Support Vector Machine (SVM) swarm intelligence algorithm Transfer function

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EVALUATION AND ENHANCEMENT OF NATURE-INSPIRED OPTIMIZATION FOR ANTENNA IMPEDANCE MATCHING

EVALUATION AND ENHANCEMENT OF NATURE-INSPIRED OPTIMIZATION F...

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作者： Homsup, Nathawut PennState University Libraries

学位级别：Doctor of Philosophy

Advancements in the technology of communication systems have led to increased requirements and constraints on antennas in terms of cost, bandwidth, and many other performance parameters. In the design of any antenna, a significant amount of time and resources is spent on impedance matching. There are two widely used approaches to impedance matching. The first is to modify the antenna geometry by identifying the appropriate degree of freedom within the structure. The second is the lumped element approach, which modifies the antenna with a passive network consisting of inductors and capacitors to the impedance mismatch between the source and the antenna load. In the second approach, different types of circuits can be used in order to obtain the best results, such as a two lumped elements L network, a T network, a three lumped elements Pi network, or a network with more lumped elements. In order to achieve the desired results from these circuits, we must choose the optimal values of inductor, capacitor, and transducer elements. To optimize parameters in impedance matching network design, two of the most promising approaches are those of the nature-inspired genetic algorithm (GA) and swarm intelligence algorithms. Examples of these swarm algorithms are particle swarm optimization (PSO), ant colony optimization (ACO), and firefly algorithm (FA). Nature-inspired optimization methods have been used in antenna design for decades in the form of GA and PSO. However, many other algorithms are relatively new to the antenna design problems, even though, these algorithms have already been successfully applied to many other problems and have gained interest in numerous engineering and scientific research *** dissertation compares the described optimization methods by using them to minimize the maximum VSWR over the frequencies 3.5-3.85MHz of the impedance matching network that connects to a high-frequency dipole antenna. In order to satisfy the match over a finite bandwidt

关键词： Antenna Design Impedance Matching Firefly algorithm Nature-Inspired Optimization swarm intelligence algorithm Modified Firefly algorithm

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Particle swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem 11

Particle Swarm Optimization with Gaussian Disturbance-based ...

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11th Joint International Information Technology and Artificial intelligence Conference, ITAIC 2023

作者： Zhang, Zhiming Sui, Qingya Qi, Lingyu Song, Yaotong Gao, Shangce University of Toyama Faculty of Engineering Toyama-shi930-8555 Japan UM-SJTU Joint Institute Shanghai JiaoTong University Shanghai200240 China

ISBN: (纸本)9798350333664

Single-objective optimization, especially with con- straints, is the most common class of problems in biology, society, and energy. Among various optimization algorithms, swarm intelligence algorithms is undoubtedly an effective methods to solve this type of problem. In this study, we propose a novel swarm intelligence optimization method, namely GuLo, which adopts Gaussian random disturbance into elite population-based particle swarm optimization, which leads the improvement of local search. Comprehensive experimental results on a typical single-objective constrained optimization problem benchmark shows that GuLo has the outstanding performance than other state-of-the-art meta-heuristic optimization approaches. © 2023 IEEE.

关键词： Meta-heuristic Optimization problem swarm intelligence algorithm

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Coordinated Path Planning for UAVs Based on Sheep Optimization

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第5期37卷 816-830页

作者： YANG Liuqing WANG Pengfei ZHANG Yong Research Institute of Pilotless Aircraft Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China Key Laboratory of Advanced Technology for Small and Medium-Sized UAV Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle ***,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population *** an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative *** simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of *** with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.

关键词： multi-UAV cooperation path planning swarm intelligence algorithm multi-population improved sheep optimization(ISO)

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FATA: An efficient optimization method based on geophysics

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NEUROCOMPUTING 2024年 607卷

作者： Qi, Ailiang Zhao, Dong Heidari, Ali Asghar Liu, Lei Chen, Yi Chen, Huiling Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China

An efficient swarm intelligence algorithm is proposed to solve continuous multi-type optimization problems, named the fata morgana algorithm (FATA). By mimicking the process of mirage formation, FATA designs the mirage light filtering principle (MLF) and the light propagation strategy (LPS), respectively. The MLF strategy, combined with the definite integration principle, drives the algorithmic population to enhance FATA's exploration capability. The LPS strategy, combined with the trigonometric principle, drives the algorithmic individual to improve the algorithm's convergence speed and exploitation capability. These two search strategies can better use FATA's population and individual search capabilities. The FATA is compared with a broad array of competitive optimizers on 23 benchmark functions and IEEE CEC 2014 to verify the optimization capability. This work is designed separately for qualitative analysis, exploration and exploitation competence analysis, the analysis of avoiding locally optimal solutions, and comprehensive comparison experiments. The experimental results demonstrate the comprehensiveness and competitiveness of FATA for solving multi-type functions. Meanwhile, FATA is applied to three practical engineering optimization problems to evaluate its performance. Then, the algorithm obtains better results than its counterparts in engineering problems. According to the results, FATA has excellent potential to be used as an efficient computer-aided tool for dealing with practical optimization tasks. Source codes and related files are available at https://***/*** and other websites.

关键词： Fata morgana algorithm The mirage light filtering principle Light propagation strategy swarm intelligence algorithm Engineering optimization

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Decentralized Coordination Dispatch Model Based on Chaotic Mutation Harris Hawks Optimization algorithm

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ENERGIES 2022年第10期15卷 3815页

作者： Wang, Yuanyuan Yu, Zexu Dou, Zhenhai Qiao, Mengmeng Zhao, Ye Xie, Ruishuo Liu, Lianxin Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China

Aiming at the economic dispatch problem for an interconnected system with wind power integration, and in order to realize the goals of system economy and improvement of the cross-regional consumption level of wind energy, a decentralized coordination dispatch model is established in this paper. In this model, a DC tie-line is cut by the branch cutting method and used as a coupling variable. A virtual upper-level dispatch center is established, and the economic dispatch problem to be solved is decomposed into a master optimization problem for the upper-level dispatch center and subsidiary optimization problems for the lower-level dispatch centers. For solving this model, an improved Harris hawks optimization (HHO) algorithm called the chaotic mutation Harris hawks optimization (CMHHO) algorithm is proposed. In the CMHHO algorithm, tent mapping and the "DE/pbad-to-pbest/1" strategy are introduced, and a new nonlinear escape energy factor adjustment is proposed. Through an algorithm comparison experiment and a simulation experiment with two examples, the superiority of the CMHHO algorithm, the effectiveness of the proposed model and the applicability of the CMHHO algorithm to the proposed model are verified. The model proposed is of great significance for solving the economic dispatch problem for an interconnected system with wind power integration.

关键词： decentralized coordinated dispatch analytical target cascading swarm intelligence algorithm Harris hawks optimization optimization DC tie-line

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