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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 849-862页

作者： Ye‐Qun Wang Jian‐Yu Li Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan School of Computer Science and Engineering South China University of TechnologyGuangzhouChina School of Software Engineering South China University of TechnologyGuangzhouChina Hanyang University AnsanSouth Korea

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial ***,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally *** tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)***,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best ***,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate ***,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational *** SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

关键词： deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation

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Research on error compensation of robot based on multi-hole measurement technology and particle swarm optimization

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The Journal of China Universities of Posts and Telecommunications 2021年第4期28卷 88-96页

作者： Wang Xianlun Sun Yuxuan Wang Dong Hu Xiaowei School of Electrical and Mechanical Engineering Qingdao University of Science and TechnologyQingdao 266000China

Robot error compensation is a technique for enhancing the positioning accuracy of the system. This paper presented an error measuring technique for serial robots based on the multi-hole measuring method, combined with the intelligent particle swarm optimisation(PSO) to obtain the optimal solution of the robot’s error compensation values, thereby improving the positioning accuracy of the robot. In the experiment, the robot error was measured using self-made multi-hole measuring plates and probes, and the experimental data were combined with PSO for the error comprehensive analysis. The results showed that on this type of serial robot, the multi-hole measuring method and PSO algorithm had obvious error compensation effects, which effectively improved the positioning accuracy of the robot, with the error reduced by 35% after compensation.

关键词： error model multi-hole measuring technique particle swarm optimisation error compensation

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Adaptive Simplified Chicken swarm Optimization Based on Inverted S-Shaped Inertia Weight

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Chinese Journal of Electronics 2022年第2期31卷 367-386页

作者： GU Yanchun LU Haiyan XIANG Lei SHEN Wanqiang School of Science Jiangnan University Wuxi Engineering Technology Research Center for Biological Computing Jiangnan University

Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic chicken swarm optimization algorithm, an adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight(ASCSO-S) is proposed. Firstly, a simplified chicken swarm optimization algorithm is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the *** experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.

关键词： ASCSO-S particle swarm optimisation adaptive updating strategy convergence speed solution stability Richards model swarm intelligence solution accuracy convergence parameter estimation updating process adaptive simplified chicken swarm optimization algorithm inverted S-shaped inertia weight

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PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

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BIOCELL 2023年第2期47卷 373-384页

作者： WEI WANG YANRONG PEI SHUI-HUA WANG JUAN MANUEL GORRZ YU-DONG ZHANG School of Computing and Mathematical University of LeicesterLeicesterLE17RHUK Huai’an Tongji Hospital Huai’an223000China Department of Signal Theory Networking and CommunicationsUniversity of GranadaGranada52005Spain

Since 2019,the coronavirus disease-19(COVID-19)has been spreading rapidly worldwide,posing an unignorable threat to the global economy and human *** is a disease caused by severe acute respiratory syndrome coronavirus 2,a single-stranded RNA virus of the genus *** virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter *** the increase in the number of confirmed COVID-19 diagnoses,the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly *** learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this *** tuning is essential in training such models and significantly impacts their final performance and training ***,traditional hyperparameter tuning methods are usually time-consuming and *** solve this issue,we introduce particle swarm optimisation to build a PSO-guided Self-Tuning Convolution Neural Network(PSTCNN),allowing the model to tune hyperparameters ***,the proposed approach can reduce human ***,the optimisation algorithm can select the combination of hyperparameters in a targeted manner,thus stably achieving a solution closer to the global ***,the PSTCNN can obtain quite excellent results,with a sensitivity of 93.65%±1.86%,a specificity of 94.32%±2.07%,a precision of 94.30%±2.04%,an accuracy of 93.99%±1.78%,an F1-score of 93.97%±1.78%,Matthews Correlation Coefficient of 87.99%±3.56%,and Fowlkes-Mallows Index of 93.97%±1.78%.Our experiments demonstrate that compared to traditional methods,hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

关键词： COVID-19 SARS-CoV-2 particle swarm optimisation Convolutional neural network Hyperparameters tuning

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optimisation for interconnected energy hub system with combined ground source heat pump and borehole thermal storage

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Frontiers in Energy 2018年第4期12卷 529-539页

作者： Da HUO Wei WEI Simon Le BLOND Department of Electronic & Electrical Engineering University of BathClaverton DownBathBA27AYUK

Ground source heat pumps (GSHP) give zero-carbon emission heating at a residential level. However, as the heat is discharged, the temperature of the ground drops, leading to a poorer efficiency. Borehole inter-seasonal thermal storage coupled with GSHP maintains the efficiency at a high level. To adequately utilize the high performance of combined GSHP and the borehole system to further increase system efficiency and reduce cost, such a combined heating system is incorporated into the interconnected multi-carrier system to support the heat load of a community. The borehole finite element (FE) model and an equivalent borehole transfer function are proposed and respectively applied to the optimisation to analyze the variation of GSHP performance over the entire optimisation time horizon of 24 h. The results validate the borehole transfer function, and the optimisation computation time is reduced by 17 times compared with the optimisation using the FE model.

关键词： borehole thermal storage energy hub ground source heat pumps (GSHP) particle swarm optimisation

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Remarks on the Efficiency of Bionic optimisation Strategies

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Journal of Mathematics and System Science 2014年第3期4卷 139-154页

作者： Simon Gekeler Julian Pandtle Rolf Steinbuch Christoph Widmann Reutlingen University Department of Engineering Reutlingen Research Institute 72 762 Reutlingen Germany Now with Wafios AG 72764 Reutlingen Germany

Bionic optimisation is one of the most popular and efficient applications of bionic engineering. As there are many different approaches and terms being used, we try to come up with a structuring of the strategies and compare the efficiency of the different methods. The methods mostly proposed in literature may be classified into evolutionary, particle swarm and artificial neural net optimisation. Some related classes have to be mentioned as the non-sexual fern optimisation and the response surfaces, which are close to the neuron nets. To come up with a measure of the efficiency that allows to take into account some of the published results the technical optimisation problems were derived from the ones given in literature. They deal with elastic studies of frame structures, as the computing time for each individual is very short. General proposals, which approach to use may not be given. It seems to be a good idea to learn about the applicability of the different methods at different problem classes and then do the optimisation according to these experiences. Furthermore in many cases there is some evidence that switching from one method to another improves the performance. Finally the identification of the exact position of the optimum by gradient methods is often more efficient than long random walks around local maxima.

关键词： Bionic optimisation efficiency evolutionary optimisation particle swarm optimisation artificial neural nets.

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Co-digestion of Waste Coffee and Cocoa Hulls: Modeling of Biogas Production by the particle swarm Method

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Journal of Energy and Power Engineering 2023年第4期17卷 121-135页

作者： Michel SOUOP TAGNE George Elambo NKENG Paul Nestor DJOMOU DJONGA Yvette NONO JIOKAP Department of Process Engineering National School of Agro-industrial SciencesP.O.Box 455NgaoundereCameroon Department of Civil Environmental and Architectural EngineeringUniversity of PadovaVia Francesco Marzolo935151 PadovaItaly Department of Textile and Leather Engineering National Advanced School of Engineering of MarouaP.O.Box 58MarouaCameroon Department of Energy Engineering University Institute of TechnologyUniversity of NgaoundereP.O.Box 455NgaoundereCameroon

Energy is a crucial material for the development of our *** to sufficient energy remains a major concern for developing countries,particularly those in sub-Saharan *** major challenge lies in access to clean,environmentally friendly,quality and low-cost energy in different households in our *** cope with this vast energy gap,many households are dependent on fossil *** Cameroon,the consumption of wood for the supply of energy is increasing by 4%per ***,approximately 80%of households in Cameroon depend on woody biomass as the sole main source of energy supply in Cameroon and demand is growing over *** view of the climatic variations that our countries,particularly Cameroon,undergo through deforestation,the use of wood as a source of energy is expensive and harmful to the environment,hence the urgency of replacing wood with renewable *** is one of the most versatile sources of renewable *** an industrial scale,it is important to automate the process *** main objective of the present work is to model the anaerobic digestion of coffee and cocoa hulls using the particle swarm optimisation *** using the organosolv process was *** resulted in 48%lignin removal and 22%cellulose *** the pretreated biomass,the maximum production rate was 21 NmLCH4 per day with a biomethane yield of 90 NmLCH4/*** represents an enhancement of 117%in biomethane yield.A positive flammability test was recorded after the 10th day of retention ***,the data collected during anaerobic digestion allowed implementation of a two-phase mathematical *** thirteen parameters of the model were estimated with particle swarm optimisation method in *** model was able to simulate the biomethane production kinetics and variation of volatile fatty acid concentration.

关键词： Lignocellulosic biomass organosolv process anaerobic digestion mathematical model particle swarm optimisation

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