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hybrid intelligent algorithm and its application in geological hazard risk assessment

NEUROCOMPUTING 2015年第PartB期149卷 847-853页

作者： Liu, Ming He, Yude Wang, Jiaxin Lee, Heow Pueh Liang, Yanchun Jilin Univ Coll Comp Sci & Technol Changchun 130023 Peoples R China Changchun Univ Technol Coll Basic Sci Changchun Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 117548 Singapore

This paper introduces a novel intelligent biologically inspired computational method developed to facilitate the accurate assessment of geological hazard risk (GHR). The hybrid intelligent algorithm (HIA) combines Genetic algorithm (GA), Particle Swarm Optimization (PSO) and Back Propagation (BP) neural network. GA is adopted to initialize the network connection weights and thresholds of BP and PSO is used to update them in the iteration process. In simulations based on hazard monitoring data from Jilin Province in northeastern China, the HIA method provided increased accuracy compared to established methods using BP neural networks. As GHR assessment grows in acceptance among the international risk assessment community, improved hybrid methods such as HIA will promote more effective planning in emergency response, environmental management, land use, and development. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Geological hazard risk hybrid intelligent algorithm BP neural network Genetic algorithm Particle swarm optimization

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hybrid intelligent algorithm aided energy consumption optimization in smart grid systems with edge computing

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intelligent SYSTEMS WITH APPLICATIONS 2024年 24卷

作者： Li, Shuangwei Xie, Yang Shi, Mingming Zheng, Xian Lu, Yongling State Grid Jiangsu Elect Power China Southern Powe Nanjing 570100 Jiangsu Peoples R China

The rapid proliferation of smart grid systems necessitates efficient management of energy resources, particularly in the context of mobile edge computing (MEC) networks. This paper presents a novel approach to optimize the energy consumption in smart grid systems with the integration of edge computing, employing a hybrid intelligent algorithm (HIA) empowered by particle swarm optimization (PSO). The primary objective is to enhance the sustainability and operational efficiency of smart grid infrastructures by minimizing the energy consumption in the MEC networks. The proposed HIA utilizes PSO to dynamically allocate computational tasks and manage resources among edge devices based on real-time demand fluctuations. This adaptive approach aims to achieve the optimal load balancing and energy efficiency across the smart grid ecosystem. By leveraging the PSO's ability to iteratively refine solutions and adapt to changing environmental conditions, the algorithm optimizes the energy consumption while maintaining requisite service levels and reliability. Simulation experiments and case studies validate the effectiveness of the proposed PSO-based HIA in reducing the energy consumption without compromising system other performances. The results demonstrate substantial improvements in the energy efficiency, illustrating the feasibility and benefits of employing intelligent algorithms tailored for edge computing environments within smart grid systems. This research contributes to advancing sustainable smart grid technologies by introducing a robust framework for energy optimization through hybrid intelligent algorithms.

关键词： hybrid intelligent algorithm Energy consumption Edge computing

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hybrid intelligent algorithm for Human Resources Demand Forecasting

Hybrid Intelligent Algorithm for Human Resources Demand Fore...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wang, Baocheng Baiyin Highway Development Center of Gansu Province Gansu Baiyin730900 China

ISBN: (纸本)9798350309614

Enterprise human resource demand forecasting (HRDF) is an important part of enterprise human resource management. This article takes enterprise human resource demand forecasting as the research object, and studies it based on fuzzy neural network (FNN) and time series forecasting methods. First of all, based on the fuzzy neural network, according to the influencing factors of the enterprise's human resource needs and the non-linear relationship between each influencing factor, the article establishes the model of the fuzzy neural network, and estimates the relevant parameters. Secondly, in terms of time series forecasting methods, the article combines gray theory, Markov model and genetic algorithm, and establishes a time series forecasting model through data forecasting and analysis. Finally, the feasibility and effectiveness of the above research method are verified by experimental simulation. The experimental results show that the hybrid intelligent algorithm can increase the prediction accuracy to the highest 99.7%. © 2023 IEEE.

关键词： Fuzzy Neural Network Human Resources Demand Forecast hybrid intelligent algorithm Technology Time Series

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Dynamic Optimization Model of Flatness Target Curve Based on hybrid intelligent algorithm

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STEEL RESEARCH INTERNATIONAL 2017年第6期88卷 n/a-N.PAG页

作者： Yan, Zhu-Wen Bu, He-Nan Zhang, Dian-Hua Northeastern Univ State Key Lab Rolling & Automat 3-11 Wenhua Rd Shenyang Peoples R China

By the intelligent strategy of dynamic optimization in flatness target curve, the potential of the flatness control system can be completely fulfilled and the satisfying strip flatness can be speedily obtained. The traditional intervention pattern is static, open-loop, non-intelligent. In this paper, a new dynamic adjustment model of flatness target curve has been designed, by which the initial setpoint can be real-timely regulated. The closed-loop control system, equipped with the function of intelligent optimization and dynamic regulation, has been developed. A hybrid intelligent algorithm called ZOGE, in which both the searching definiteness and randomness are taken into account and the multipoint searching and single-point searching are simultaneously carried through, has been proposed. The equivalent factor of flatness target curve has been optimized by function module HYBALO, which is developed on the basis of the hybrid intelligent algorithm and is operated on CFC configuration language in SIMATIC TDC controller. The testing in the field has been conducted through a 1450 mm 5-stand tandem cold mill. The preliminary finding from the main results of testing in the field is reported: by means of the intelligent compensation model of flatness target curve, the content flatness quality of strip with thin thickness in slow rolling speed is effectively guaranteed. The maximum compensation efficiency of speed is 72.19%. The maximum compensation efficiency of thickness is 98.37%. The ability of eliminating strip flatness defect in dynamic regulation mode of flatness target curve is more powerful than the ability of eliminating strip flatness defect in conventional regulation mode.

关键词： hybrid intelligent algorithm flatness control flatness target curve compensation efficiency equivalence factor

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Inventory lot-sizing with supplier selection using hybrid intelligent algorithm

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APPLIED SOFT COMPUTING 2008年第4期8卷 1523-1529页

作者： Moghadam, Mohammad Reza Sadeghi Afsar, Amir Sohrabi, Babak Univ Tehran Dept Informat Technol Management Fac Management Tehran Iran Univ Tehran Dept Ind Management Fac Management Tehran Iran Qom Univ Dept Ind Management Fac Management Qom Iran

In supply chain management (SCM), multi-product and multi-period models are usually used to select the suppliers. In the real world of SCM, however, there are normally several echelons which need to be integrated into inventory management. This paper presents a hybrid intelligent algorithm, based on the push SCM, which uses a fuzzy neural network and a genetic algorithm to forecast the rate of demand, determine the material planning and select the optimal supplier. We test the proposed algorithm in a case study conducted in Iran. (C) 2007 Elsevier B.V. All rights reserved.

关键词： hybrid intelligent algorithm inventory lot-sizing fuzzy neural network (FNN) genetic algorithm (GA) principle component analysis (PCA)

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Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm

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ENERGY SCIENCE & ENGINEERING 2021年第3期9卷 330-342页

作者： Li, Xia Cui, Tao Huang, Kun Ma, Xin Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploita Chengdu 610500 Peoples R China China Petr Pipeline Engn Co LTD Langfang Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang Sichuan Peoples R China

Compressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification;a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE-WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.

关键词： compressor station optimization DE hybrid intelligent algorithm load sharing MINLP WOA

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A NN-Based hybrid intelligent algorithm for a Discrete Nonlinear Uncertain Optimal Control Problem

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NEURAL PROCESSING LETTERS 2017年第2期45卷 457-473页

作者： Ding, Chunxiao Sun, Yun Zhu, Yuanguo Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China Liaocheng Univ Sch Math Sci Liaocheng 252059 Shandong Peoples R China

A discrete-time nonlinear optimal control system with uncertain perturbation is studied in this paper. A value-iteration-based hybrid intelligent algorithm (HIA), incorporating heuristic dynamic programming algorithm and uncertain simulation, is presented. And some characters of the HIA are provided. Two neural networks are used in the algorithm: a critic network is aimed at approximating the objective function, as well as an active network is used to obtain an uncertain optimal control law. As an application, a schistosomiasis compartment SI model is solved to illustrate the feasibility and effectiveness of the HIA.

关键词： hybrid intelligent algorithm Uncertainty theory Optimal control Neural networks Schistosomiasis

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A hybrid intelligent algorithm and rescheduling technique for job shop scheduling problems with disruptions

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第5-8期65卷 1141-1156页

作者： Zhang, Liping Gao, Liang Li, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

In most real manufacturing environment, schedules are usually inevitable with the presence of various unexpected disruptions. In this study, a new rescheduling technique based on a hybrid intelligent algorithm is developed for solving job shop scheduling problems with random job arrivals and machine breakdowns. According to the dynamic feature of this problem, a new initialization method is proposed to improve the performance of the hybrid intelligent algorithm, which combines the advantage of genetic algorithm and tabu search. In order to solve the difficulty of using the mathematical model to express the unexpected disruptions, a simulator is designed to generate the disruptions. The performance measures investigated respectively are: mean flow time, maximum flow time, mean tardiness, maximum tardiness, and the number of tardy jobs. Moreover, many experiments have been designed to test and evaluate the effect of different initializations in several disruption scenarios. Finally, the performance of the new rescheduling technique is compared with other rescheduling technologies in various shop floor conditions. The experimental results show that the proposed rescheduling technique is superior to other rescheduling techniques with respect to five objectives, different shop load level, and different due date tightness. The results also illustrate that the proposed rescheduling technique has a good robustness in the dynamic manufacturing environment.

关键词： Job shop scheduling problem Rescheduling technique Random job arrivals Machine breakdowns hybrid intelligent algorithm

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Permeability Prediction of the Tight Sandstone Reservoirs Using hybrid intelligent algorithm and Nuclear Magnetic Resonance Logging Data

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2017年第4期42卷 1643-1654页

作者： Zhu, Lin-qi Zhang, Chong Wei, Yang Zhang, Chao-mo Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Educ Minist Wuhan 430100 Hubei Province Peoples R China Yangtze Univ Geophys & Oil Resource Inst Wuhan 430100 Hubei Province Peoples R China

The existing permeability evaluation models with nuclear magnetic resonance log always have low accuracy for permeability prediction of tight sandstone reservoirs. In this paper, the relationship between nuclear magnetic resonance (NMR) T-2 spectrum and permeability was derived based on the transverse relaxation theory and Kozeny-Carman equation. A hybrid intelligent algorithm that combines Adaboost algorithm, adaptive rain forest optimization algorithm and improved back propagation neural network was proposed based on the theoretical analysis. Permeability experiments were conducted on 196 rock specimens in the tight sandstone reservoirs, and the corresponding T-2 spectrum data of the NMR logging were extracted for modeling. The permeability evaluation results by this model showed that the proposed algorithm was superior to other algorithms in optimizing the initial neural network. Through the combination of hybrid intelligent algorithm with NMR logging data, the proposed permeability prediction model has higher accuracy than the existing ones, and its evaluation accuracy can be further improved with the development of artificial intelligence.

关键词： NMR logging Permeability evaluation Theoretical derivation Adaptive rain forest optimization algorithm hybrid intelligent algorithm

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A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel programming problems

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SWARM AND EVOLUTIONARY COMPUTATION 2013年 8卷 26-32页

作者： Wan, Zhongping Wang, Guangmin Sun, Bin Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China

In this paper, a hybrid intelligent algorithm by combining the particle swarm optimization (PSO) with chaos searching technique (CST) is presented for solving nonlinear bilevel programming problems. The bilevel programming is transformed into a single level programming problem by use of the KKT conditions of the lower level problem. Then, the hybrid intelligent algorithm is proposed to solve the transformed problem. Our approach embeds the CST into PSO. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. Secondly, an optimization problem is solved by CST to judge whether the particle is feasible or not. In each iteration, all the feasible particles are ranked in ascending order. Particles in the front of list are updated by PSO, while particles in the end of list are updated by CST. The CST used here is not only to enhance the particles but also to improve the diversity of the particle swarm so as to avoid PSO trapping the local optima. Finally, the hybrid intelligent algorithm is commented by illustrating the numerical results on several benchmark problems from the references. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Nonlinear bilevel programming problems hybrid intelligent algorithm Particle swarm optimization Chaos search technique

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