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Micro-EDM optimization through particle swarm algorithm and artificial neural network

PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2022年 73卷 63-70页

作者： Quarto, Mariangela D'Urso, Gianluca Giardini, Claudio Univ Bergamo Dept Management Informat & Prod Engn Via Pasubio 7-B I-24044 Bergamo Italy

In the present study, an artificial neural network (ANN) together with a heuristic algorithm, called particle swarm optimization (PSO), was used to set up a methodology for selecting the optimal process parameters for the mu EDM process. The developed methodology is characterized by a double direction functionality responding to different industry needs. Usually, in the industrial scenario, the operators are bound by the project specifications or by the limited availability of time. For this reason, a methodology tested only on a specific workpiece material, that involves limited input parameters or developed for the optimization of a single performance is limiting. The developed 2-steps model leaves operators free to establish which factors to impose for the optimization and allows to define the best solution for the production of a part. The validation of the model shows a good fit between predicted and experimental results.

关键词： Artificial neural network particle swarm optimization algorithm Micro-EDM

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A novel mapping algorithm for three-dimensional network on chip based on quantum-behaved particle swarm optimization

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Frontiers of Computer Science 2017年第4期11卷 622-631页

作者： Cui HUANG Dakun ZHANG Guozhi SONG School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin 300387 China

Mapping of three-dimensional network on chip is a key problem in the research of three-dimensional network on chip. The quality of the mapping algorithm used di- rectly affects the communication efficiency between IP cores and plays an important role in the optimization of power consumption and throughput of the whole chip. In this paper, ba- sic concepts and related work of three-dimensional network on chip are introduced. Quantum-behaved particle swarm op- timization algorithm is applied to the mapping problem of three-dimensional network on chip for the first time. Sim- ulation results show that the mapping algorithm based on quantum-behaved particle swarm algorithm has faster con- vergence speed with much better optimization performance compared with the mapping algorithm based on particle swarm algorithm. It also can effectively reduce the power consumption of mapping of three-dimensional network on chip.

关键词： three-dimensional network on chip mapping al-gorithm quantum-behaved particle swarm optimization al-gorithm particle swarm optimization algorithm low powerconsumption

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A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training

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APPLIED MATHEMATICS AND COMPUTATION 2007年第2期185卷 1026-1037页

作者： Zhang, Jing-Ru Zhang, Jun Lok, Tat-Ming Lyu, Michael R. Chinese Acad Sci Hefei Inst Intelligent Machines Intelligent Comp Lab Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Chinese Univ Hong Kong Informat Engn Dept Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Comp Sci & Engn Dept Shatin Hong Kong Peoples R China

The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP) algorithm, also referred to as PSO-BP algorithm, is proposed to train the weights of feedforward neural network (FNN), the hybrid algorithm can make use of not only strong global searching ability of the PSOA, but also strong local searching ability of the BP algorithm. In this paper, a novel selection strategy of the inertial weight is introduced to the PSO algorithm. In the proposed PSO-BP algorithm, we adopt a heuristic way to give a transition from particle swarm search to gradient descending search. In this paper, we also give three kind of encoding strategy of particles, and give the different problem area in which every encoding strategy is used. The experimental results show that the proposed hybrid PSO-BP algorithm is better than the Adaptive particle swarm optimization algorithm (APSOA) and BP algorithm in convergent speed and convergent accuracy. (c) 2006 Elsevier Inc. All rights reserved.

关键词： particle swarm optimization algorithm feedforward neural network BP algorithm adaptive particle swarm optimization

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A diversity enhanced hybrid particle swarm optimization and crow search algorithm for feature selection

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APPLIED INTELLIGENCE 2023年第17期53卷 20535-20560页

作者： Osei-kwakye, Jeremiah Han, Fei Amponsah, Alfred Adutwum Ling, Qing-Hua Abeo, Timothy Apasiba Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang Peoples R China Univ Profess Studies Informat Technol Studies Dept Accra Ghana Univ Mines & Technol Comp Sci & Engn Dept Tarkwa Ghana Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang Peoples R China Tamale Tech Univ Fac Appl Sci & Technol Dept Comp Sci Tamale Ghana

Feature Selection (FS) is choosing a subcategory of features purposed to construct a machine learning model. Among the copious existing FS algorithms, Binary particle swarm optimization algorithm (BPSO) is prevalent with applications in several domains. However, BPSO suffers from premature convergence that affects exploration, resulting in dilapidation. In this current work, we boost the exploration of BPSO, incorporating the intelligence of crows to hide their food sources from other crows and predators and maintain diversity by implementing a clustering strategy. The clustering technique guarantees that the starting population is evenly distributed over the feature space while including more promising features. Additionally, suppose a crow realizes another crow or a predator is tracking it. In that case, the crow moves randomly to evict the stalker, leading to a better exploration of unexplored regions within the search space. We named the proposed method Hybrid particle swarm optimization and Crow Search algorithm with clustering initialization strategy (HPSOCSA-CIS). To evaluate the performance of HPSOCSA-CIS, 15 standard UCI datasets are utilized, and the outcomes are compared with recently proposed hybrid and standard optimization algorithms. From observation, HPSOCSA-CIS outperforms the comparing approaches for feature selection challenges on representative datasets that fall in the three-category based on dimensions. The HPSOCSA-CIS improves performance in terms of mean classification accuracy by 8.87%, 17.5%, and 21.90% on Low, medium, and high dimensional datasets, respectively.

关键词： particle swarm optimization algorithm Crow search algorithm Clustering Feature selection

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An artificial-intelligence-driven product design framework with a synergistic combination of Genetic algorithm and particle swarm optimization

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SOFT COMPUTING 2023年第23期27卷 17621-17638页

作者： Liu, Yuge Kim, Kiesu Silla Univ Sch Design Busan 46958 South Korea

In recent years, China's rapid advancements in artificial intelligence (AI) technology have positioned it as a global leader in information processing, intelligent chip development, and deep learning technologies. The pervasive impact of AI has led to transformative shifts in various domains of life, with its integration across industries substantially enhancing overall production efficiency. Particularly evident in product design, the infusion of AI technology has catalyzed significant productivity improvements. This research investigates the amalgamation of Genetic algorithms (GA) and particle swarm optimization (PSO) within the framework of AI technology to establish a robust product design system. The system aims to enhance the efficiency and efficacy of design processes by leveraging the complementary strengths of GA and PSO. This integration has propelled product design into accelerated evolution and modernization. The study delves into AI's application of deep neural networks in product design within the context of the digital age. Artificial intelligence technology is used in product design in the context of the Internet. First, the concept of AI technology is briefly introduced, and DNN algorithms are described in detail, including the PSO and Genetic algorithms. Second, the product design and development process based on artificial intelligence technology, design guidelines, and technical features are mainly described. Finally, using the seat as the research object, the application effect of artificial intelligence technology in product design is analyzed, and 10 subjective evaluation questions are set to experiment. Regarding material texture, the highest score for the B3 Barcelona chair is 4.3. Users prefer leather seats with diverse materials, and the score for painted wood materials is lower. From the product structure and shape analysis, the highest score is the B3 Barcelona chair, which has a value of 4.1, and the lowest score is the B1 red and blue

关键词： Artificial intelligence Internet particle swarm optimization algorithm Genetic algorithm Product design

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Hybrid Bat & particle swarm algorithm for optimization of labyrinth spillway based on half & quarter round crest shapes

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FLOW MEASUREMENT AND INSTRUMENTATION 2019年 66卷 209-217页

作者： Ferdowsi, Ahmad Farzin, Saeed Mousavi, Sayed-Farhad Karami, Hojat Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran

optimization is an interesting method that finds the optimal response (i.e., objective function) by searching the decision variables in the search space. The present study deals with the optimal design of a labyrinth spillway (LS) having half-round or quarter-round crest shape. For optimal the design of the spillway geometry, an evolutionary Hybrid algorithm (HA) of Bat algorithm (BA) and particle swarm optimization (PSO) algorithm was developed. By this procedure, the worst responses of one algorithm are replaced by the best responses of the other algorithm. This HA does not get trapped in local minima and has high convergence rate concerning the optimal absolute response. Results of using HA showed that LS with half-round crest requires less concrete, compared to quarter-round crest. Moreover, the discharge capacity for LS with half-round crest was more than the quarter-round crest. The HA significantly reduced the computational time for the optimal design of LS with half-round or quarter-round crest, as compared to BA or PSO.

关键词： optimization Labyrinth spillway Crest shape Hybrid algorithm Bat algorithm particle swarm optimization algorithm

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A particle swarm algorithm optimization-based SVM-KNN algorithm for epileptic EEG recognition

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 11233-11249页

作者： Wang, Xiaoying Ling, Yu Ling, Xiang Li, Xianghuan Li, Zhicheng Hu, Kunpeng Dai, Min Zhu, Jia Du, Yuxiao Yang, Qintai Sun Yat Sen Univ Affiliated Hosp 3 Informat Ctr Guangzhou 510630 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510630 Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Dept Otolaryngol Head & Neck Surg Guangzhou Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Dept Gen Surg Guangzhou Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Dept Tradit Chinese Med Guangzhou Peoples R China Zhejiang Normal Univ Key Lab Intelligent Educ Technol & Applicat Zheji Jinhua Zhejiang Peoples R China

Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (>= 95%) with only a small number of training samples, which has a good clinical application value.

关键词： empirical modal decomposition epilepsy EEG recognition K-nearest neighbor particle swarm optimization algorithm support vector machine

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Research on data mining algorithm based on neural network and particle swarm optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 2921-2926页

作者： Fei, Xianju Tian, Guozhong Changzhou Inst Technol Sch Comp Informat & Engn Changzhou Jiangsu Peoples R China

In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of data mining. So this time we proposed a data mining algorithm based on neural network and particle swarm optimization. At the beginning, we calculated the global kernel function of differentiated distributed data mining and mixed to build the mining decision model. The training error was used as the constraint condition of mining optimization to realized data optimization mining. The results showed that the differential distributed data mining with this algorithm has higher accuracy and stronger convergence.

关键词： Neural network algorithm particle swarm optimization algorithm data mining algorithm

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Research on Collision Detection algorithm Based on particle swarm optimization

Research on Collision Detection Algorithm Based on Particle ...

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5th International Conference on E-learning and Games, Edutainment 2010

作者： Zhao, Wei Li, Li-Jun Chen, Cheng-Shou Jilin Agr Univ Sch Informat Technol Changchun 130118 Peoples R China Changchun Univ Technol Sch Comp Sci & Engn Changchun 130012 Peoples R China

ISBN: (纸本)9783642145322

This paper presents an algorithm based on OBB bounding box and particle swarm hybrid collision detection algorithm. algorithm use hierarchical bounding boxes OBB Rapid exclude some objects do not intersect, only the nodes in the collision of the use of particle swarm optimization in the searching. To play a level bounding box algorithm and improved particle swarm-based collision detection algorithm Random their own advantages. Finally, Experimental results demonstrate the effectiveness of hybrid collision detection algorithm.

关键词： Virtual Reality Collision Detection particle swarm optimization algorithm Bounding Volume Hierarchies

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FPFTS: A joint fuzzy particle swarm optimization mobility-aware approach to fog task scheduling algorithm for Internet of Things devices

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SOFTWARE-PRACTICE & EXPERIENCE 2021年第12期51卷 2519-2539页

作者： Javanmardi, Saeed Shojafar, Mohammad Persico, Valerio Pescape, Antonio Univ Napoli Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy Univ Surrey 5G Innovat Ctr Inst Commun Syst Guildford Surrey England

In the Internet of Things (IoT) scenario, the integration with cloud-based solutions is of the utmost importance to address the shortcomings resulting from resource-constrained things that may fall short in terms of processing, storing, and networking capabilities. Fog computing represents a more recent paradigm that leverages the wide-spread geographical distribution of the computing resources and extends the cloud computing paradigm to the edge of the network, thus mitigating the issues affecting latency-sensitive applications and enabling a new breed of applications and services. In this context, efficient and effective resource management is critical, also considering the resource limitations of local fog nodes with respect to centralized clouds. In this article, we present FPFTS, fog task scheduler that takes advantage of particle swarm optimization and fuzzy theory, which leverages observations related to application loop delay and network utilization. We evaluate FPFTS using an IoT-based scenario simulated within iFogSim, by varying number of moving users, fog-device link bandwidth, and latency. Experimental results report that FPFTS compared with first-come first-served (respectively, delay-priority) allows to decrease delay-tolerant application loop delay by 85.79% (respectively, 86.36%), delay sensitive application loop delay by 87.11% (respectively, 86.61%), and network utilization by 80.37% (respectively, 82.09%), on average.

关键词： fog computing Internet of Things Mamdani fuzzy inference system particle swarm optimization algorithm resource management task scheduling

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