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A cluster-based intelligence ensemble learning method for classification problems

INFORMATION SCIENCES 2021年 560卷 386-409页

作者： Cui, Shaoze Wang, Yanzhang Yin, Yunqiang Cheng, T. C. E. Wang, Dujuan Zhai, Mingyu Dalian Univ Technol Sch Econ & Management Dalian 116023 Peoples R China Univ Elect Sci & Technol China Sch Econ & Management Chengdu 610064 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Kowloon Hong Kong Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

Classification is a vital task in machine learning. By learning patterns of samples of known categories, the model can develop the ability to distinguish the categories of samples of unknown categories. Noticing the advantages of the clustering method in cluster structure analysis, we combine the clustering and classification methods to develop the novel cluster-based intelligence ensemble learning (CIEL) method. We use the clustering method to analyze the inherent distribution of the data and divide all the samples into clusters according to the characteristics of the dataset. Then, for each specific cluster, we use differ-ent classification algorithms to establish the corresponding classification model. Finally, we integrate the prediction results of each base classifier to form the final prediction result. In view of the problem of parameter sensitivity, we use a swarm intelligence algorithm to optimize the key parameters involved in the clustering, classification, and ensemble stages in order to boost the classification performance. To assess the effectiveness of CIEL, we per -form tenfold cross-validation experiments on the 24 benchmark datasets provided by UCI and KEEL. Designed to improve the performance of the classifiers, CIEL outperforms other popular machine learning methods such as naive Bayes, k-nearest neighbors, random for -est, and support vector machine. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Ensemble learning Classification algorithm Combination strategy Clustering algorithm swarm intelligence algorithm

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Monthly Runoff forecasting using A Climate-driven Model Based on Two-stage Decomposition and Optimized Support Vector Regression

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WATER RESOURCES MANAGEMENT 2024年第14期38卷 5701-5722页

作者： Jia, Zhuo Peng, Yuhao Li, Qin Xiao, Rui Chen, Xue Cheng, Zhijin Nanchang Univ Sch Infrastruct Engn Nanchang 330031 Peoples R China Nanchang Univ Key Lab Poyang Lake Environm & Resource Utilizat Minist Educ Nanchang 330031 Peoples R China Jiangxi Acad Sci Inst Energy Res Nanchang 330096 Peoples R China

Accurate and reliable monthly runoff forecasting is crucial for water resource management, but the increasing non-stationarity of runoff series poses new challenges to the development of forecasting models. To overcome these problems, this study proposes a novel climate-driven hybrid model based on two-stage decomposition ***, called ICEEMDAN-SVMD-ESPSO-SVR. Firstly, the original runoff is decomposed into a set of components by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and high-frequency components identified through entropy analysis are further decomposed into multiple subcomponents by successive variational mode decomposition (SVMD). Secondly, the input variables are selected from the candidate set consisting of runoff, subcomponents and key climate factors by using correlation coefficient and mutual information, respectively. Thirdly, support vector regression (SVR) is employed to forecast each sub-component, and particle swarm optimization algorithm using eagle strategy (ESPSO) is used to select each model's parameters. Finally, the forecasted values of all sub-components are aggregated as the final result. Monthly runoff data from three different hydrological stations in China's Poyang Lake Basin are employed to evaluate the performance of the proposed hybrid model and other comparable models. Results indicate that the proposed hybrid model outperforms single models and other combination models based on single-stage decomposition, and the correlation coefficient method is more suitable for input variable selection in climatedriven models, thus confirming the effectiveness of two-stage decomposition, the ESPSO algorithm, and input variable selection in enhancing modeling accuracy. Therefore, the proposed hybrid model is a feasible and promising new method for monthly runoff forecasting.

关键词： Input variable selection Monthly runoff forecasting Support vector regression swarm intelligence algorithm Two-stage decomposition

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Sintered silicon carbide grinding surface roughness prediction based on deep learning and neural network

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第7期44卷 1-13页

作者： Yang, Jie Zhang, Liqiang Liu, Gang Gao, Qiuge Qian, Long Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Surface roughness is vital to high-precision parts, and it directly affects performance and service time of parts. So, in the grinding process, roughness control and prediction of grinding surface is particularly important. However, it is difficult to accurately predict surface roughness due to interlaced influence of large factors in grinding process. This problem can be solved due to the unique working principle of neural network;thus, in this paper, for the task of ground surface roughness, an integration sparrow search algorithm-based deep belief net model (SSA-DBN) was established, based on data obtained from simulation and experiment. SSA refers to a type of swarm intelligence algorithm, and DBN refers to deep belief net and belongs to deep learning, respectively. The purpose of swarm intelligence algorithm is to optimize network weight, compared to DBN model and SSA-BP network model, that is, DBN without optimization and back-propagation neural network (BPNN) with optimization, for analyzing their performance separately. Ground surface roughness prediction based on SSA-DBN model effectiveness was proven through training. At the same time, compared to SSA-BP network model and DBN model, SSA-DBN model performance was the best.

关键词： Roughness Neural network swarm intelligence algorithm Deep learning

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Optimal Foraging algorithm Based on Differential Evolution

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IEEE ACCESS 2020年 8卷 19657-19678页

作者： Fu, Yanming Zhang, Weizhi Qu, Chiwen Huang, Baohua Guangxi Univ Comp & Elect Informat Coll Nanning 530004 Peoples R China Baise Univ Sch Informat Engn Baise 533000 Peoples R China

The optimal foraging algorithm (OFA) was proposed by summarizing the rules of the animal foraging behavior in a group. Therefore OFA also has the defects of the swarm intelligence algorithm, such as easy to trap into local optimum and low convergence accuracy. In order to overcome these defects, an optimal foraging algorithm based on differential evolution (DEOFA) is proposed. The differential evolution mechanism contains mutation and crossover operators. The mutation and crossover operators are used to accelerate the convergence speed and global search capability of the OFA. The mutation operator is adopted to perform mutation operations centered on the optimal individual of each iteration to raise the convergence accuracy of the OFA. The test results of 30 benchmark functions show that the performance of DEOFA is better than nine compared algorithms in search accuracy, convergence speed and robustness. In order to verify the effectiveness of the DEOFA in solving practical problems, DEOFA is applied to solve the 0-1 knapsack problem. The test results in the six examples of 0-1 knapsack problems indicate that the DEOFA achieves better performance in accuracy, stability and high dimension.

关键词： swarm intelligence algorithm optimal foraging algorithm differential evolution mutation and crossover 0-1 knapsack problem

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Enhancing firefly algorithm with courtship learning

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INFORMATION SCIENCES 2021年 543卷 18-42页

作者： Peng, Hu Zhu, Wenhua Deng, Changshou Wu, Zhijian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

The firefly algorithm (FA) is a nature-inspired heuristic optimization algorithm based on the luminescence and attraction behavior of fireflies. Although the FA can effectively solve complex optimization problems, it suffers from premature convergence because of its simple full attraction model. However, in nature, fireflies exhibit luminous behavior to attract mates. The attractiveness between fireflies of opposite sexes depends not only on the light intensity they emit, but also on their individual size, location, and other factors. In the original FA, all fireflies are assumed to be the same, i.e., they have no gender-based difference, which is not true biologically. Therefore, in this paper, we proposed a novel courtship learning (CL) framework to enhance the performance of the FA. In the proposed framework, the population is divided into female and male subpopulations. The female archiving mechanism is adopted to select excellent fireflies, which are assumed to be female fireflies. When the selected male firefly emits light that is less bright than that of the current firefly, a female individual will be selected from the female archive to guide the movement of the selected male firefly. Comprehensive experiments are conducted on the CEC 2013 benchmark set and the proposed CL framework is integrated with other advanced FA variants to verify its effects. Experimental results confirm that the proposed framework significantly enhances the performance of the original FA and advanced FA variants. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Firefly algorithm swarm intelligence algorithm Global optimization Courtship learning

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Whale Optimization algorithm Strategies for Higher Interaction Strength T-Way Testing

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Computers, Materials & Continua 2022年第10期73卷 2057-2077页

作者： Ali Abdullah Hassan Salwani Abdullah Kamal Z.Zamli Rozilawati Razali Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Computing College of Computing and Applied SciencesUniversiti Malaysia PahangPekan26600PahangMalaysia

Much of our daily tasks have been computerized by machines and sensors communicating with each other in *** is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of *** testing(CT)can be used in this case to reduce risks and ensure conformance to *** existing metaheuristic-based solutions aim to assist the test suite generation for combinatorial testing,also known as t-way testing(where t indicates the interaction strength),viewed as an optimization *** previous research,while helpful,only investigated a small number of interaction strengths up to t=*** lightweight applications,research has demonstrated good fault-finding ***,the number of interaction strengths considered must be higher in the case of interactions that generate large amounts of *** to resource restrictions and the combinatorial explosion challenge,little work has been done to produce high-order interaction *** this context,the Whale Optimization algorithm(WOA)is proposed to generate high-order interaction *** ensure that WOA conquers premature convergence and avoids local optima for large search spaces(owing to high-order interaction),three variants of WOA have been developed,namely Structurally Modified Whale Optimization algorithm(SWOA),Tolerance Whale Optimization algorithm(TWOA),and Tolerance Structurally Modified Whale Optimization algorithm(TSWOA).Our experiments show that the third strategy gives the best performance and is comparable to existing state-of-thearts based strategies.

关键词： Software testing optimization problem swarm intelligence algorithm combinatorial optimization IoT

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Energy-aware virtual machine placement based on a holistic thermal model for cloud data centers

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 161卷 302-314页

作者： Lin, Jianpeng Lin, Weiwei Wu, Wentai Lin, Wenjun Li, Keqin South China Univ Technol Dept Comp Sci & Engn Guangzhou 510000 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510000 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

As energy-intensive infrastructures, data centers (DCs) have become a pressing challenge for managers due to their significant energy consumption and carbon emissions. Information technology (IT) and cooling systems contribute the most to energy consumption. Energy-aware virtual machine (VM) scheduling methods have been widely demonstrated to reduce energy consumption and operating costs in DCs. However, as realistic DCs exhibit complex power and thermodynamic behaviors, existing works cannot provide efficient measures to optimize computing and cooling power consumption simultaneously. To overcome this challenge, we construct a holistic thermal model (including CPU and server inlet thermal models) to accurately represent the nonuniform, dynamic thermal environment. Subsequently, this work proposes a thermal model-based energy-aware VM placement method (TEVP) to minimize the holistic energy consumption of the DCs, considering resource and thermal constraints. We develop a novel hybrid swarm intelligence algorithm (DE-ERPSO) combining differential evolution (DE) and particle swarm optimization with an elite re-selection mechanism (ERPSO) to explore more energy-efficient VM placement schemes. Extensive experiments are conducted on an extended CloudSim to validate the performance of the proposed TEVP using real-world workload traces (PlanetLab and Azure). Results show that TEVP saves over 5.6% of the total energy consumption over the advanced baselines while maintaining low thermal violations.

关键词： Data center Thermal model VM placement swarm intelligence algorithm Energy saving

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Improved butterfly optimisation algorithm based on guiding weight and population restart

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL intelligence 2021年第1期33卷 127-145页

作者： Guo, Yanju Liu, Xianjie Chen, Lei Hebei Univ Technol Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

Butterfly Optimisation algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution.

关键词： Butterfly optimisation algorithm meta-heuristic swarm intelligence algorithm guiding weight population restart

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MKELM: Mixed Kernel Extreme Learning Machine using BMDA optimization for web services based heart disease prediction in smart healthcare

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2022年第10期25卷 1180-1194页

作者： Sheeba, Adlin Padmakala, S. Subasini, C. A. Karuppiah, S. P. St Josephs Inst Technol Dept Comp Sci & Engn Chennai Tamil Nadu India St Josephs Coll Engn Dept MBA Chennai Tamil Nadu India

In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA-MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA-MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.

关键词： Machine learning healthcare M-health heart disease prediction BMDA swarm intelligence algorithm MKELM web services

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A novel elitist fruit fly optimization algorithm

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SOFT COMPUTING 2023年第8期27卷 4823-4851页

作者： He, Jieguang Peng, Zhiping Qiu, Jinbo Cui, Delong Li, Qirui Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming Peoples R China Guangdong Univ Petrochem Technol Coll Elect Informat Engn Maoming Peoples R China

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed.

关键词： swarm intelligence algorithm Fruit fly optimization algorithm Elite guidance Boundary information Population diversity

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