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Diagnosis model of pancreatic cancer based on fusion of distribution estimation algorithm and genetic algorithm

NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 5425-5434页

作者： Wang, Xusheng Li, Xiaofeng Chen, Xing Cao, Congjun Xian Univ Technol Xian 710048 Shaanxi Peoples R China Heilongjiang Int Univ Dept Informat Engn Harbin 150025 Heilongjiang Peoples R China

Since the beginning of the twenty-first century, people's living standards have been continuously improved, followed by changes in diet structure and living habits. These changes have affected the body's endocrine system, causing lesions in the pancreatic tissue. Among these pancreatic tissue diseases, pancreatic cancer is the most harmful to human health because of its inability to find and high mortality within 1 year. At present, in the diagnosis of pancreatic cancer, medical imaging and pathological puncture are the main methods of diagnosis. These methods have a high diagnostic rate for patients with advanced pancreatic cancer, but it is difficult to apply to the diagnosis of early pancreatic cancer. In response to these problems, this paper proposes a pancreatic cancer diagnosis model based on the fusion of distribution estimation algorithm and genetic algorithm. By collecting pathological data of patients with pancreatic cancer from a hospital oncology, pathological data include clinical manifestations of pancreatic cancer patients, serum tumor markers, etc., after data preprocessing, input models, and then use different machine learning classification algorithms to make pancreatic cancer for diagnosis. By evaluating the diagnosis results of each classification algorithm, an optimal classification algorithm is obtained and applied to the diagnosis model of pancreatic cancer. The results show that compared with other classification algorithms, the model using classification algorithm has the highest accuracy, recall rate and harmonic mean, and the diagnostic performance is the best. The results show that the diagnostic model constructed in this paper has a very high application value in the early auxiliary pre-diagnosis of pancreatic cancer.

关键词： distribution estimation algorithm Genetic algorithm Pancreatic cancer diagnosis Model evaluation

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Leak Detection Based on estimation of distribution algorithm and Wavelet Transform

Leak Detection Based on Estimation of Distribution Algorithm...

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2nd International Conference on Measurement, Information and Control (ICMIC)

作者： Wang, Xiufang Guo, Yingwei Gao, Running Northeast Petr Univ Sch Elect Engn & Informat Daqing Peoples R China

ISBN: (纸本)9781479913909

Because of the problems that low location positioning accuracy and more number of iterations of traditional artificial intelligence algorithms that applied to detect the gas leakage. This paper proposes a distribution estimation algorithm based on wavelet transform and the method is used to diagnosis of pipeline leakage. Through the wavelet analysis to the signal, singular value information about upstream and downstream signals can be got and be used as the initial population in the method. And probability model of the individual's distribution in the space is established and sample the model. Gradually, the optimal probability vector within the solution space can be improved. The simulation results show that distribution estimation algorithm was applied to gas pipeline leakage detection positioning has a better position precision and good fault tolerance.

关键词： The leak positioning distribution estimation algorithm Wavelet transform

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Association Rule Mining Based on estimation of distribution algorithm for Blood Indices

Association Rule Mining Based on Estimation of Distribution ...

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International Conference on Computer Network, Electronic and Automation (ICCNEA)

作者： Zhang, Xinyu Sui, Guanghu Xue, Botu Cui, Jianjiang Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China Northeastern Univ Inst Intelligent Syst Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538639818

To come over the limitations of Apriori algorithm and association rule mining algorithm based on Genetic algorithm (GA), this paper proposed a new association rule mining algorithm based on the population-based incremental algorithm (PBIL), which is a kind of distribution estimation algorithms. The proposed association rule-mining algorithm keeps the advantages of GA mining association rules in coding and the fitness function. Through using probability vector possessing learning properties to update the population, the algorithm increases the convergence speed and enhances the searching ability, compared to GA. In the experiment of mining association, rules in blood indices data, PBIL algorithm performs better not only in running time, convergence speed, but also achieve better searching results. Meanwhile, this paper proposed a parallel algorithm for association rule mining based on PBIL and designed a system architecture based on cloud computing for blood indices analysis, providing a good example to apply the new algorithm to cloud computing.

关键词： distribution estimation algorithm Probability vector Blood indices Parallel algorithm Cloud computing

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Optimal Trading in Local Electricity Market Based on an Enhanced Evolutionary algorithm with distribution estimation 22

Optimal Trading in Local Electricity Market Based on an Enha...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Bai, Wenlei Lezama, Fernando Lee, Kwang Y. Oracle Corp Austin TX 77042 USA Polytech Porto P-4200465 Porto Portugal Baylor Univ Waco TX 76706 USA

ISBN: (纸本)9781713872344

The evolution from passive to active distribution network, the ability to actively control distributed energy resources and to perform bilateral energy exchange has significantly complicated the market transactions in recent years due to activities among consumers and prosumers. Local electricity market (LEM) is challenged to optimize welfare as well as reduction of emissions by involving participants in energy transactions. In this paper, the trading strategy is modeled as a single-objective optimization problem to determine each agent's optimal energy trading. Modern heuristic optimization techniques have proven their efficiency and robustness in large optimization problems and thus, an enhanced artificial bee colony (EABC) incorporating a distribution estimation algorithm is proposed, which guides the search for optimum by building and sampling explicit probabilistic models of promising candidate solutions. With the help of a distribution estimation algorithm, the EABC eliminates many tuning parameters in the original ABC and increases its exploitation to obtain more robust and competitive performance. A comparative study is conducted with the basic ABC, differential evolution, and particle swarm optimization, and the EABC demonstrates its efficiency on the case study where nine agents are involved in trading energy in the day-ahead LEM. Copyright (c) 2023 The Authors.

关键词： Local electricity market Day-ahead trading Evolutionary algorithm distribution estimation algorithm Artificial bee colony Renewable energy

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Optimal Trading in Local Electricity Market Based on an Enhanced Evolutionary algorithm with distribution estimation

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IFAC-PapersOnLine 2023年第2期56卷 384-389页

作者： Wenlei Bai Fernando Lezama Kwang Y. Lee Oracle Corporation Austin TX 77042 USA Polytechnic of Porto Porto 4200-465 Portugal Baylor University Waco TX 76706 USA

关键词： Local electricity market Day-ahead trading Evolutionary algorithm distribution estimation algorithm Artificial bee colony Renewable energy

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RETRACTED: Effect of PI controllers' parameters on machine-network interaction of grid-connected PMSG system (Retracted Article)

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IET GENERATION TRANSMISSION & distribution 2019年第20期13卷 4677-4684页

作者： Habib, Arslan Aristizabal, Andres Julian Arshad, Adeel Univ Strathclyde Fac Engn Dept Elect & Elect Engn Glasgow G1 1XQ Lanark Scotland Univ Bogota Jorge Tadeo Lozano Engn Dept Cr 4 22-61 Bogota Colombia Univ Nottingham Fluids & Thermal Engn FLUTE Res Grp Nottingham NG7 2RD England

In order to study the oscillation problem of grid-connected permanent magnet direct drive wind power generation (PMSG) system, a small-signal model of PMSG system based on PQ (active and reactive powers) decoupling control is established. All the oscillation modes of the system are found out, by using eigenvalue analysis and correlation factor analysis. The characteristic frequency and the dominant state variables of each oscillation mode are considered, the optimal parameter combination of proportional-integral (PI) controller is obtained by using the distribution estimation algorithm based on Copula theory. The time-domain simulation of the MATLAB/Simulink platform verifies the existence of various modes of oscillation. The control parameters of the converter are changed in turn without changing the stability of the system, the subsynchronous control interaction;the subsynchronous oscillation and the low-frequency oscillation mode of the system are studied. The modal analysis of the PMSG system model is carried out by using the optimal PI controller parameters, and the simulation results verify the correctness of the proposed model. This type of theoretical/practical approach has not been taken before to resolve the oscillation problems of grid-connected PMSG.

关键词： eigenvalues and eigenfunctions wind power plants permanent magnet generators power generation control power grids PI control time-domain analysis modal analysis oscillations synchronous generators power system stability Matlab power system simulation reactive power control machine-network interaction grid-connected PMSG system oscillation problem small-signal model active powers reactive powers eigenvalue analysis correlation factor analysis dominant state variables proportional-integral controller subsynchronous control interaction subsynchronous oscillation low-frequency oscillation mode modal analysis PMSG system model optimal PI controller parameters permanent magnet direct drive wind power generation system decoupling control distribution estimation algorithm Copula theory time-domain simulation MATLAB-Simulink system stability

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An Effective Memetic algorithm for the Distributed Integrated Scheduling of Tree-Structured Products

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INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS 2021年第1-4期30卷 2150001-2150001页

作者： Gao, Yilong Xie, Zhiqiang Jia, Qing Yu, Xu Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

Aiming at the distributed integrated scheduling of complex products with tree structure, a memetic algorithm-based distributed integrated scheduling algorithm is proposed. Based on the framework of the memetic algorithm, the algorithm uses a distributed estimation algorithm for global search and performs a local search strategy based on the critical operation set for the current optimal solution obtained in each evolutionary generation. A bi-chain-based individual representation method is presented and a simple greedy insertion-based decoding method is given;two position-based probability models are built, which are used to describe the distribution of the operation priority and factory assignment, respectively. Based on the designed probability models, two learning-based updating mechanisms and an improved sampling method are given, which ensures that the population evolves towards a promising region. In order to enhance the searchability for the superior solutions, nine disturbance operators based on the critical operation set are presented. The parameters are determined by the design-of-experiment (DOE) test, and the effectiveness of the proposed algorithm is verified by comparative experiments.

关键词： Processing and assembly tree-structured products distributed integrated scheduling memetic algorithm distribution estimation algorithm

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A Hybrid Improved Lion Swarm Optimization algorithm 23

A Hybrid Improved Lion Swarm Optimization Algorithm

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Proceedings of the 2023 2nd International Symposium on Computing and Artificial Intelligence

作者： Keqin Jiang Mingyan Jiang School of information science and engineering Shandong university China

ISBN: (纸本)9798400708954

Abstract: When solving multimodal optimization problems, the lion swarm optimization (LSO) algorithm will face many problems, such as low individual diversity, slow search speed, premature convergence or even falling into local extremum. The traditional approach is to introduce chaotic search, gaussian mutation or other mutation strategies to enhance the local search ability of the LSO algorithm. These improvements have been verified and the performance of the algorithm can be improved to a certain extent. However, these improved strategies lack effective use of population information, which will still affect the performance of the algorithm in search speed and search accuracy. To solve this problem, on the basis of the above-mentioned improved algorithm, this paper introduces the distribution estimation algorithm and proposes a hybrid improved LSO algorithm. The hybrid improved algorithm analyzes and learns the structure of the problem by constructing a probability model for the dominant group, and guides the efficient optimization of individuals in the population according to this information. It is verified in five standard test functions and ten test functions of IEEE CEC 2021. Compared with the traditional improved LSO algorithm, the results show that the hybrid improved LSO algorithm is superior.

关键词： Chaotic search distribution estimation algorithm Lion swarm optimization algorithm Mutation strategy

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