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Multiobjective Evolution of Fuzzy Rough Neural Network via distributed parallelism for Stock Prediction

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第5期28卷 939-952页

作者： Cao, Bin Zhao, Jianwei Lv, Zhihan Gu, Yu Yang, Peng Halgamuge, Saman K. Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Beijing Univ Chem Technol Beijing Adv Innovat Ctr Soft Matter Sci & Engn Beijing 100029 Peoples R China Goethe Univ Frankfurt Dept Chem D-60323 Frankfurt Germany Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Univ Melbourne Sch Elect Mech & Infrastruct Engn Parkville Vic 3010 Australia

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of prediction precision and network simplicity, each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions, and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance the optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements the existing fuzzy rough neural network and the long short-term memory network.

关键词： distributed parallelism evolutionary neural network fuzzy rough neural network (FRNN) multiobjective evolution stock price prediction

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Deployment optimization for 3D industrial wireless sensor networks based on particle swarm optimizers with distributed parallelism

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2018年 103卷 225-238页

作者： Cao, Bin Zhao, Jianwei Lv, Zhihan Liu, Xin Kang, Xinyuan Yang, Shan Hebei Univ Technol Sch Comp Sci & Engn Tianjin 300401 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China

For wireless sensor networks (WSNs), traditional studies on deployment problems center upon 2D plane or 3D full space. However, practical situations are more complex, and simplifications may hinder real-world application. In this paper, we study the scenario of a 3D industrial space with obstacles (i.e., devices). Heterogeneous directional sensor nodes and relay nodes are deployed to maximize coverage and prolong lifetime, respectively. Specifically, sensor nodes are deployed for the maximization of coverage;after the positions of sensor nodes are generated, we deploy relay nodes to maximize the lifetime. A modified 3D coverage model and a lifetime model with reliability constraint are presented to facilitate the mathematical analysis of the deployment problem. For the NP-hard deployment problem, two particle swarm optimizers, the cooperative coevolutionary particle swarm optimization 2 (CCPSO2) and the comprehensive learning particle swarm optimizer (CLPSO), are employed. To reduce the computation time, distributed parallelism based on message passing interface (MPI) is conducted by dividing the 3D deployment space. Extensive experimentations are conducted by using various numbers of sensor nodes and relay nodes, and thorough understandings are obtained w.r.t. both the deployment problem and the optimizers.

关键词： Industrial wireless sensor networks (IWSNs) Heterogeneous directional sensor nodes Relay nodes Deployment optimization Coverage Lifetime Particle swarm optimization (PSO) Message passing interface (MPI) distributed parallelism

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Evolutionary Neural Architecture Search and Its Applications in Healthcare

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 143-185页

作者： Xin Liu Jie Li Jianwei Zhao Bin Cao Rongge Yan Zhihan Lyu School of Economics and Management Hebei University of TechnologyTianjin300401China School of Artificial Intelligence Hebei University of TechnologyTianjin300401China School of Electrical Engineering Hebei University of TechnologyTianjin300401China Department of Game Design Uppsala UniversityUppsalaS-75105Sweden

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error *** architecture search(NAS)attempts to detect effective architectures without human *** algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible *** multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered ***,hardware-accelerated NAS methods can improve the efficiency of the *** existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for *** this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS *** performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health *** for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare *** neural network has been successfully used in healthcare,but it lacks *** data is highly sensitive,and privacy leaks are frequently reported in the healthcare *** solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy ***,we also point out future research directions for evolutionary ***,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

关键词： Neural architecture search evolutionary computation large-scale multiobjective optimization distributed parallelism healthcare

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Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第10期30卷 4190-4200页

作者： Cao, Bin Zhao, Jianwei Liu, Xin Arabas, Jaroslaw Tanveer, Mohammad Singh, Amit Kumar Lv, Zhihan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Warsaw Univ Technol Inst Comp Sci PL-00665 Warsaw Poland Indian Inst Technol Indore Discipline Math Simrol 453552 India Natl Inst Technol Patna Dept Comp Sci & Engn Patna 800005 Bihar India Uppsala Univ S-75105 Uppsala Sweden

The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.

关键词： Fuzzy neural networks Optimization Fuzzy logic Neural networks Rough sets Time series analysis Artificial neural networks distributed parallelism explainability gene expression programming (GEP) interval type-2 fuzzy rough neural network (IT2FRNN) multiobjective evolution

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Accelerating real-time object detection in high-resolution video surveillance

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第18期35卷

作者： Wang, Yuefeng Mao, Kuang Chen, Tong Yin, Yanglong He, Shuibing Chen, Gang Res Ctr Intelligent Comp Syst Zhejiang Lab Bldg 101818 Wenyi Xi Lu Hangzhou Zhejiang Peoples R China State Grid Zhejiang Elect Power Co Ltd Informat & Commun Branch Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

As various algorithms and models spring up, object detection-based deep learning for image analysis has become an established technology in the field of computer vision. Furthermore, object detection for high-resolution videos derived from ubiquitous surveillance cameras draws many researchers' attention due to its practical significance and the challenge on detecting accuracy and speed. Existing object detection methods mainly resize and compress each frame in the video and then apply the detection algorithm, but with the method, the detection for small objects in dense scenes is far from satisfactory in terms of accuracy because small objects are reduced to pixel-level sizes in compressed frame images. In order to fix this issue, we propose AROD, a parallel detection framework based on adaptive image cropping. Unlike the traditional first-compression-and-then-detection methods, AROD adopts adaptive image cropping for distributed parallel detection. In this way, AROD significantly improves the accuracy of small object detection in dense scenes with acceptable overhead. In our experimental evaluation, YOLOv2-tiny model equipped with AROD reaches 29.29 FPS along with high detection accuracy. Experimental results verify that AROD ensures the high throughput of real-time video analytics while maintaining high detection accuracy.

关键词： distributed parallelism high‐ resolution small object detection

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Establishing massively parallel models to examine the influence of cell heterogeneity on tumor growth

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 71卷

作者： Tanade, Cyrus Putney, Sarah Randles, Amanda Duke Univ Wilkinson 325534 Res Dr Durham NC 27708 USA

Parallel 3D cellular automaton models of tumor growth can efficiently capture emergent morphology. We extended a 2D growth model to 3D to examine the influence of symmetric division in heterogeneous tumors on growth dynamics. As extending to 3D severely increased time-to-solution, we parallelized the model using N-body, lattice halo exchange, and adaptive communication schemes. Supplementing prior work from Tanade et al. (2022), we demonstrated over 55x speedup and evaluated performance on & LE;30 nodes of Stampede2. This work established a framework to parametrically study 3D growth dynamics, and of the cancer phenotypes we studied, the parallel model better scaled when tumor boundaries were radially symmetric.

关键词： Cellular automaton Heterogeneous tumor growth Cancer stem cells Parallel computing distributed parallelism

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Multiobjective feature selection for microarray data via distributed parallel algorithms

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 100卷 952-981页

作者： Cao, Bin Zhao, Jianwei Yang, Po Yang, Peng Liu, Xin Qi, Jun Simpson, Andrew Elhoseny, Mohamed Mehmoode, Irfan Muhammad, Khan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Liverpool John Moores Univ Dept Comp Sci Liverpool Merseyside England Mansoura Univ Fac Computers & Informat Mansoura Egypt Univ Bradford Dept Media Design & Technol Fac Engn & Informat Bradford BD7 1DP W Yorkshire England Sejong Univ Dept Software Seoul 143747 South Korea

Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency. (C) 2019 Published by Elsevier B.V.

关键词： Microarray dataset High dimension Multiobjective feature selection distributed parallelism Feature redundancy

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3-D Deployment Optimization for Heterogeneous Wireless Directional Sensor Networks on Smart City

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第3期15卷 1798-1808页

作者： Cao, Bin Zhao, Jianwei Yang, Po Yang, Peng Liu, Xin Zhang, Yuan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Liverpool John Moores Univ Dept Comp Sci Liverpool L3 5UA Merseyside England Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China

The development of smart cities and the emergence of three-dimensional (3-D) urban terrain data have introduced new requirements and issues to the research on the 3-D deployment of wireless sensor networks. We study the deployment issue of heterogeneous wireless directional sensor networks in 3-D smart cities. Traditionally, studies on the deployment problem of WSNs focus on omnidirectional sensors on a 2-D plane or in full 3-D space. Based on 3-D urban terrain data, we transform the deployment problem into a multiobjective optimization problem, in which objectives of Coverage, Connectivity Quality, and Lifetime, as well as the Connectivity and Reliability constraints, are simultaneously considered. A graph-based 3-D signal propagation model employing the line-of-sight concept is used to calculate the signal path loss. Novel distributed parallel multiobjective evolutionary algorithms (MOEAs) are also proposed. For verification, real-world and artificial urban terrains are utilized. In comparison with other state-of-the-art MOEAs, the novel algorithms could more effectively and more efficiently address the deployment problem in terms of optimization performance and operation time.

关键词： distributed parallelism heterogeneous wireless directional sensor networks (HWDSNs) line-of-sight (LOS) signal propagation model (SPM) wireless sensor network (WSN) deployment

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distributed Parallel Particle Swarm Optimization for Multi-Objective and Many-Objective Large-Scale Optimization

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IEEE ACCESS 2017年 5卷 8214-8221页

作者： Cao, Bin Zhao, Jianwei Lv, Zhihan Liu, Xin Yang, Shan Kang, Xinyuan Kang, Kai Hebei Univ Technol Sch Comp Sci & Engn Tianjin 300401 Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China Hebei Univ Technol Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Hebei Univ Technol Tianjin 300401 Peoples R China

With the advent of big data era, complex optimization problems with many objectives and large numbers of decision variables are constantly emerging. Traditional research about multi-objective particle swarm optimization (PSO) focuses on multi-objective optimization problems (MOPs) with small numbers of variables and less than four objectives. At present, MOPs with large numbers of variables and many objectives (greater than or equal to four) are constantly emerging. When tackling this type of MOPs, the traditional multi-objective PSO algorithms have low efficiency. Aiming at these multi-objective large-scale optimization problems (MOLSOPs) and many-objective large-scale optimization problems (MaOLSOPs), we need to explore thoroughly parallel attributes of the particle swarm, and design the novel PSO algorithms according to the characteristics of distributed parallel computation. We survey the related research on PSO: multi-objective large-scale optimization, many-objective optimization, and distributed parallelism. Based on the aforementioned three aspects, the multi-objective large-scale distributed parallel PSO and many-objective large-scale distributed parallel PSO methodologies are proposed and discussed, and the other future research trends are also illuminated.

关键词： Particle swarm optimization (PSO) multi-objective optimization many-objective optimization large-scale optimization distributed parallelism

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A distributed Parallel Cooperative Coevolutionary Multiobjective Evolutionary Algorithm for Large-Scale Optimization

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2017年第4期13卷 2030-2038页

作者： Cao, Bin Zhao, Jianwei Lv, Zhihan Liu, Xin Hebei Univ Technol Sch Comp Sci & Engn Tianjin 300401 Peoples R China Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Guangzhou 510275 Guangdong Peoples R China Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China UCL Dept Comp Sci London WC1E 6EA England Hebei Univ Technol Tianjin 300401 Peoples R China

A considerable amount of research has been devoted to multiobjective optimization problems. However, few studies have aimed at multiobjective large-scale optimization problems (MOLSOPs). To address MOLSOPs, which may involve big data, this paper proposes a message passing interface MPI -based distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm (DPCCMOEA). DPCCMOEA tackles MOLSOPs based on decomposition. First, based on a modified variable analysis method, we separate decision variables into several groups, each of which is optimized by a subpopulation (species). Then, the individuals in each subpopulation are further separated to several sets. DPCCMOEA is implemented with MPI distributed parallelism and a two-layer parallel structure is constructed. We examine the proposed algorithm using the multiobjective test suites Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group. In comparison with cooperative coevolutionary generalized differential evolution 3 and multiobjective evolutionary algorithm based on decision variable analyses, which are state-of-the-art cooperative coevolutionary multiobjective evolutionary algorithms, experimental results show that the novel algorithm has better performance in both optimization results and time consumption.

关键词： Cooperative coevolution decomposition distributed parallelism large-scale optimization message passing interface (MPI) variable grouping

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