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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2024年第1期5卷 589-598页

作者： Kou, Jiamin Li, Ke Zheng, Leyu Beijing Union Univ Coll Smart City Beijing 100101 Peoples R China

A human learning optimization with Diversified Search (DSHLO) algorithm is proposed to address the limitation of existing human learning optimization algorithms, such as smaller search space, and local optima due to the replication of optima in both individual and social learning operations. By introducing diversified search strategies, the DSHLO algorithm uses different methods to explore different solution spaces by simulating different human learning styles. Firstly, chaotic mapping is employed to enhance the population's likelihood of evolution. Secondly, inductive learning operators enrich the population diversity by combining learned individual and social knowledge with new one. Thirdly, the stochastic learning operator, based on the triangular walking strategy, increases the local optimization capability of the algorithm. Finally, the social learning operator, based on social hierarchy dominance, improves the convergence rate. The proposed algorithm is validated on the CEC2017 test set by comparison with nine baseline algorithms. The experimental results show that the DSHLO algorithm achieves faster convergence speeds and higher accuracy in most of the cases. Experiments on a supply chain planning and scheduling application prove that the proposed algorithm is also eligible to solve the practical engineering problems.

关键词： chaotic mapping learning operators human learning optimization algorithm diversified search diversified search learning operators diversified search

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An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive human learning optimization algorithm and Its Application to Financial Markets Forecasting 13th

An Intelligent Weighted Fuzzy Time Series Model Based on a S...

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International Conference on Advanced Data Mining and Applications (ADMA)

作者： Yang, Ruixin Xu, Mingyang He, Junyi Ranshous, Stephen Samatova, Nagiza F. North Carolina State Univ Raleigh NC 27695 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA Shanghai Univ Shanghai Peoples R China

ISBN: (纸本)9783319691794;9783319691787

Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.

关键词： Weighted fuzzy time series human learning optimization algorithm Financial markets forecasting

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An effective multi-model based nonlinear control for USC power plant

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ISA TRANSACTIONS 2024年 147卷 350-359页

作者： Cheng, Chuanliang Peng, Chen Xie, Xiangpeng Wang, Ling Shanghai Univ Sch Mechatron Engn & Automat Shanghai 201900 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

Energy efficiency optimization for the ultra supercritical (USC) boiler -turbine unit is a major concern in the field of power generation. In order to deal with the nonlinearity and slow dynamic response problems, a new nonlinear control method is proposed which integrates internal model control (IMC) and generalized predictive control (GPC) into a unified framework. Specifically, through a long short-term memory (LSTM) neural network based IMC, the system achieves rapid convergence to the vicinity of the desired setpoint, significantly enhancing the response speed. Then, by a composite weighted human learning optimization network based nonlinear generalized predictive control (CWHLO-GPC), high -accuracy tracking performance is achieved. Finally, an example on a 1000MW USC power plant demonstrates the proposed method can achieve fast and stable dynamic response under large load variation.

关键词： Ultra supercritical unit Internal model control Long short-term memory neural network human learning optimization algorithm Generalized predictive control

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Improved human Optimisation algorithm for Global Path Planning in AGV 5

Improved Human Optimisation Algorithm for Global Path Planni...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Wang, Qin Tang, Shaoyu Anhui Polytech Univ Sch Elect Engn Wuhu Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Aiming at the problems of slow convergence speed of human learning algorithm for optimum search, low accuracy of the optimization search, low efficiency of the path search, and lack of security in path planning, a diligent human learning optimal algorithm that integrates the particle swarm algorithm and the improved HLO algorithm is proposed. The improved algorithm is applied to global path planning, and simulation experiments prove the feasibility of the improved algorithm in AGV path planning, which has faster convergence speed and shorter planning path length than the traditional algorithm, and can effectively reduce the number of algorithm iterations.

关键词： path planning human learning optimization algorithm particle swarm algorithm AGV

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A composite weighted human learning network and its application for modeling of the intermediate point temperature in USC

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APPLIED SOFT COMPUTING 2023年 144卷

作者： Cheng, Chuanliang Peng, Chen Rong, Miao Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200444 Peoples R China

Ultra supercritical power plant (USC) is a complex system associated with nonlinearity, uncertainties and multivariable couplings. Generally, it is difficult to build an accurate model to approximate the dynamic behavior of USC. This paper presents a novel composite weighted human learning optimization network (CWHLO) to tackle the above-mentioned problem. Firstly, by fully using of the statistic characteristic of the history operating data, K-means clustering algorithm is applied to partition the raw date, which extremely reduces the operating nonlinearity. Then, an improved real-coded human learning optimization (HLO) is adopted to built linear models in local regions. Different from conventional methods, the advantage of the proposed CWHLO is that the nonlinear model of the object is effectively replaced by a real-time dynamic linear model, which is more suitable for other control methods. Finally, the CWHLO model is compared with the traditional recursive least square method (RLS), and four other meta-heuristic algorithms, to show the advantages in approximating the dynamic behavior of USC.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Composite weighted human learning optimization network Ultra supercritical power plant human learning optimization algorithm Real-time dynamic linear model

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An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting 1

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18th Industrial Conference on Data Mining (ICDM)

作者： Yang, Ruixin He, Junyi Xu, Mingyang Ni, Haoqi Jones, Paul Samatova, Nagiza North Carolina State Univ Raleigh NC 27695 USA Oak Ridge Natl Lab Oak Ridge TN USA Shanghai Univ Shanghai Peoples R China

ISBN: (数字)9783319957869

ISBN: (纸本)9783319957869;9783319957852

Given the potentially high impact of accurate financial market forecasting, there has been considerable research on time series analysis for financial markets. We present a new Intelligent Hybrid Weighted Fuzzy (IHWF) time series model to improve forecasting accuracy in financial markets, which are complex nonlinear time-sensitive systems, influenced by many factors. The IHWF model uniquely combines Empirical Mode Decomposition (EMD) with a novel weighted fuzzy time series method. The model is enhanced by an Adaptive Sine-Cosine human learning optimization (ASCHLO) algorithm to help find optimal parameters that further improve forecasting performance. EMD is a time series processing technique to extract the possible modes of various kinds of institutional and individual investors and traders, embedded in a given time series. Subsequently, the proposed weighted fuzzy time series method with chronological order based frequency and Neighborhood Volatility Direction (NVD) is analyzed and integrated with ASCHLO to determine the effective universe discourse, intervals and weights. In order to evaluate the performance of proposed model, we evaluate actual trading data of Taiwan Capitalization Weighted Stock Index (TAIEX) from 1990 to 2004 and the findings are compared with other well-known forecasting models. The results show that the proposed method outperforms the listing models in terms of accuracy.

关键词： EMD Weighted fuzzy time series human learning optimization algorithm Financial markets forecasting

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