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The Use of Graph Theory for Search Space Reduction in Contaminant Source Identification

JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2023年第2期14卷 04023016-04023016页

作者： Shahsavandi, M. Yazdi, J. Ghazizadeh, M. Jalili Mehrabadi, A. Rashidi Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran *** Iran Shahid Beheshti Univ Fac Civil Water & Environm Engn Dept Water Resources Engn Tehran *** Iran Shahid Beheshti Univ Fac Civil Water & Environm Engn Dept Water & Wastewater Tehran *** Iran

Contaminant intrusion in water distribution networks is an important concern and must be identified as soon as possible. Although optimization techniques are widely used for source identification, the performance of these methods decreases with increasing the size of the water network. To overcome this problem, this paper presents a new approach to reduce the search space, based on network topology, as a prior step to applying a search algorithm. In this method, all hydraulically connected nodes to the sensor node (with a positive detection) are identified using graph theory and considered as the new reduced search space in the optimization process. The effectiveness of the proposed method was investigated on three different benchmark water networks with different complexity and sizes. The results showed that the proposed approach can decrease the computational time by 18% to 63% while improving the success rate of contaminant source identification by 14% to 150%.

关键词： Water distribution networks Contaminant source identification Graph theory differential evolution algorithm

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New Greedy Randomized Adaptive Search Procedure based on differential evolution algorithm for solving no-wait flowshop scheduling problem

New Greedy Randomized Adaptive Search Procedure based on Dif...

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International Conference on Advanced Logistics and Transport

作者： Hanen Akrout Bassem Jarboui Abdelwaheb Rebai Patrick Siarry Faculte des Sciences Economiques et de Gestion MODILS Universite de Paris-Est Creteil LiSSi

ISBN: (纸本)9781479903146

Scheduling in a production workshop consists of assigning a number of different tasks to the existing machines with respect to the constraints of the problem in order to optimise the objective function. In this paper, we focus on the No-Wait Flow Shop Scheduling Problem (NWFSSP) with the makespan objective. We present a new hybrid algorithm NEW-GRASP-DE that combines Greedy Randomised Adaptive Search Procedure (GRASP) with differential evolution algorithm (DEA). Furthermore, we used an Iterative Local Search (ILS) as an improvement phase in the GRASP method. Our algorithm is tested on instances that have been proposed in the literature. We compared our results with those of different authors. The experimental results show the effectiveness of our algorithm.

关键词： No-wait flowshop Greedy randomized adaptive search procedure differential evolution algorithm Makespan

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Double-transfer learning-based least squares wavelet support vector machine for water quality classification

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DESALINATION AND WATER TREATMENT 2024年 320卷

作者： Wang, Le Huang, Yang Ren, Jian Zhou, Hengrui Chen, Lianghai Zhou, Hongbiao Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China

In the context of intelligent wastewater interception systems, the enhancement of decision control capability heavily relies on precise water quality classification outcomes. Nonetheless, conventional classification techniques encounter challenges in attaining satisfactory accuracy because of the intricate interplay between feature parameters of water quality and the imbalanced distribution of samples across different classes. In this paper, a least squares wavelet support vector machine (LSWSVM) is proposed with a double-transfer learning (DTL) strategy and an enhanced adaptive differential evolution (EADE) algorithm, referred to as DTL-EADE-LSWSVM. Firstly, the radial basis function is replaced by a wavelet function with multiresolution analysis to enhance the generalization ability of the LSWSVM. Secondly, three adaptive adjustment strategies for mutation factor, crossover probability and population size are designed in the EADE algorithm. Then, the EADE algorithm is adopted to obtain the optimal penalty factor and the parameter of the wavelet kernel function of the LSWSVM. Thirdly, the DTL strategy is developed to transfer the classification labels and model knowledge of the LSWSVM as well as the optimal population and optimal parameters of the EADE in the source domain to the target domain to improve the optimization efficiency and classification performance of the model in the target domain. Finally, the classification accuracy of the class imbalance samples is improved by reshaping the objective function. The experimental results indicate that the accuracy of the proposed DTL-EADE-LSWSVM classification model on balanced and imbalanced datasets is 92.5 % and 92.4 %, respectively, which is better than that of neural networks, stochastic deep forests and classical SVMs. In addition, the proposed DTL strategy has a stronger knowledge transfer capability than the single transfer learning strategy. The proposed DTL-EADE-LSWSVM is fully capable of classifying water

关键词： Wastewater intercepting system Water quality classification Least squares support vector machine differential evolution algorithm Transfer learning strategy

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Fittest Individual Referenced differential evolution algorithms for Optimization of Water Distribution Networks

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2019年第6期33卷

作者： Moosavian, Naser Lence, Barbara J. Univ British Columbia Dept Civil Engn 6250 Appl Sci Ln Vancouver BC V2T 1Z4 Canada

Design of water distribution networks (WDNs) is classified as a large combinatorial discrete nonlinear nondeterministic polynomial-hard (NP-hard) optimization problem. The main concerns associated with the optimization of WDNs are related to the nonlinearity of the discharge-head loss relationships for pipes and the discrete nature of pipe sizes. This paper proposes a new evolutionary algorithm, called fittest individual referenced differential evolution (FDE), which is significantly more efficient and reliable than other algorithms for optimization of WDN designs. The fundamental structure of FDE is similar to conventional differential evolution (DE), although the functions of the mutation and crossover operators are to exploit good solutions, and explore very different solutions, respectively, and counting and restarting mechanisms are introduced to identify and avoid local optima. These features are beneficial in WDN design because such problems may have an array of local optima that are far from one another, making finding the global optima difficult. The mutation and crossover operators are developed to accelerate convergence and, although the optimal mutation and crossover parameters may vary among networks, the efficiency of the algorithm reduces the need for adaptive parameters or population size. A sensitivity analysis is conducted for algorithm parameters, as well as counting and restarting limits, based on eight evolutionary algorithm (EA) benchmark test problems, and the efficiency of the FDE for solving 2- and 10-dimensional versions of these problems is demonstrated using the most effective values of the parameters and limits. FDE-based optimal designs of four benchmark networks-the Two-loop, Hanoi, and New York WDNs and the Balerma Irrigation Network-show that on average, a minimal number of function evaluations (or hydraulic simulations) is required to reach the best known optimum, which is less than or equal to 600 in all cases for the first three n

关键词： evolutionary algorithms differential evolution algorithm Water distribution networks Optimization

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Speed regulation of DC motors based on on-line optimum asynchronous controller tuning and differential evolution 7

Speed regulation of DC motors based on on-line optimum async...

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7th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Gabriel Villarreal-Cervantes, Miguel Rodriguez-Molina, Alejandro Inst Politecn Nacl CIDETEC Optimal Mechatron Design Lab Posgrad DeptMechatron Sect Mexico City 07700 DF Mexico Tecnol Nacl Mexico IT Tlalnepantla Res & Postgrad Div Estado De Mexico 54070 Mexico

ISBN: (纸本)9781728159539

The use of bio-inspired algorithms into controller tuning is one of the main topics in the last decades. One crucial issue when using bio-inspired algorithms to on-line tune the control system (adaptive controller tuning) is the computational time. In this work, an Asynchronous Adaptive Controller Tuning (AACT) approach is proposed to reduce the frequent activation of the tuning process, and hence, the computational cost could be reduced. This approach is based on a proposed event function, which determines the update time instant of the control parameters through the differential evolution algorithm. Comparative results with a Synchronous Adaptive Controller Tuning (SACT) approach are included in the study case of the speed regulation of the DC motor under the effects of uncertainties in the load. The SACT approach periodically updates the controller parameter at each sampling time. The comparative analysis indicates that the proposed AACT significantly reduces the controller parameter update without considerably increase the regulation error.

关键词： Optimum tuning DC motor Event based tuning differential evolution algorithm

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Intelligent Decision System of Higher Educational Resource Data Under Artificial Intelligence Technology

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2021年第5期16卷 130-146页

作者： Ma, Junhong Xian Int Univ Xian Peoples R China

It aims to apply the neural network algorithm to the mining of educational resource data and provide new ideas for the intelligent development of teaching evaluation. The Apriori algorithm is modified with the decision tree based on the research of existing university teaching evaluation system. The modified Apriori algorithm is applied to analyze the correlations of the teaching evaluation results to the teacher's age, gender, professional title, and academic qualification. The back propagation (BP) neural network model is improved as the DEA-BP based on the differential evolution algorithm (DEA). The DEA-BP model is applied to the prediction of teaching evaluation results for analysis. The results show that the execution time of the modified Apriori algorithm is significantly better than that of other models. In addition, the teacher's age (40 50 years old or 50 - 60 years old), gender (female), professional title (senior or deputy senior), and academic qualifications (undergraduate or master) have reliable correlation with the teaching evaluation results (excellent). When the DEA-BP algorithm is adopted to predict the teaching evaluation results, the average absolute error (1.05%) and the relative accuracy rate (95.44%) between its prediction value and the true value are optimal. Therefore, the Apriori algorithm and DEA-BP algorithm can intelligently extract the potential laws and knowledge in the teaching evaluation data, and provide support for teaching evaluation decisions. Thus, it exerts the role of promotion in the mining of educational resource data in universities and the intelligent development of decision-making systems.

关键词： Decision tree Apriori algorithm differential evolution algorithm BP neural network teaching evaluation

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PARAMETER ESTIMATION OF A SIMPLE, REALISTIC STOCHASTIC MODEL OF GASTRIC EMPTYING OF PELLETS UNDER FASTING CONDITIONS

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JOURNAL OF BIOLOGICAL SYSTEMS 2021年第3期29卷 561-576页

作者： Satiracoo, Pairote De Gaetano, Andrea Mahidol Univ Fac Sci Dept Math Bangkok 10400 Thailand Commiss Higher Educ Ctr Excellence Math Bangkok Thailand Natl Res Council Italy Inst Biomed Res & Innovat Via Ugo La Malfa I-153 Palermo Italy Natl Res Council Italy Inst Syst Anal & Comp Sci A Ruberti BioMatLab Biomath Lab UCSC Largo A Gemelli 8 I-00168 Rome Italy

This study is aimed to develop a parameter estimation procedure for a stochastic model of gastric emptying of pellets under fasting conditions of healthy subjects that represent the irregular decrements of gastric contents after administration of pellets. An algorithm for the identification of experimental subjects into subgroups based on their gastric emptying rates has been proposed. The parameter estimation procedure was performed with observational data of gastric emptying profiles of 19 subjects from the existing literature. The identification algorithm was validated through comparison against the original results, showing that seven subjects indicated slow gastric emptying and 12 subjects indicated fast emptying. After excluding one subject with gastric stasis, the optimal results obtained from this study classified subjects into three subgroups: two subjects with slow, five subjects with intermediate and 11 subjects with fast gastric emptying. The proposed parameter estimation procedure of the stochastic model is relatively easy to implement and can be used to analyze real patients' data. The algorithm for identification of the gastric emptying dynamics in experimental subjects could provide useful information for further investigations of long-term energy balance forecasting.

关键词： Gastric Emptying Parameter Estimation Simulated Maximum Likelihood differential evolution algorithm

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Adaptive Constrained differential evolutionary algorithm Based on Interval Probability Mechanism

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ENGINEERING LETTERS 2021年第4期29卷 1414页

作者： Wei Yanting Feng Quanxi Yuan Sainan Guilin Univ Technol Sch Sci Guilin 541006 Peoples R China

This paper aims to improve premature convergence and low precision of the differential evolution algorithm. An adaptive constrained differential evolution algorithm based on a probability interval update mechanism (PIMDE) is proposed. First, the interval probability update mechanism is developed to adaptively generate the mutation probability value, and the hybrid mutation strategy is formulated by effectively selecting complementary mutation strategies based on the mutation probability. Subsequently, the adaptive mechanism is employed to dynamically adjust the mutation strategy and parameters;as a result, the global detection and local search ability of the algorithm can be balanced, and the search efficiency of the algorithm can be enhanced. To verify determine the performance of the proposed algorithm, ten standard constrained optimization problems and five engineering optimization problems are analyzed. As revealed from experimental results, PIMDE can effectively solve COPs with high precision and robustness.

关键词： Constrained optimization differential evolution algorithm parameter self-adaptive epsilon-constraint processing technique

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Improvement for Magnetic Field Uniformity of Helmholtz Coils and its Influence on Magnetic Hyperthermia

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Tang, Yundong Ding, Yubin Jin, Tao Flesch, Rodolfo Cesar Costa Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Univ Fed Santa Catarina Automat & Syst Engn Dept BR-88040900 Florianopolis Brazil

Magnetic hyperthermia ablates tumor cells by the heat dissipated from magnetic nanoparticles (MNPs) exposed to an alternating magnetic field. The applied magnetic field plays an important role in magnetic hyperthermia, since both its amplitude and uniformity directly affect the treatment results. The amplitude of magnetic field can be easily set to a required value for reaching a desired maximum temperature, but the uniformity requires a modification in the experimental setup. Although in practice the magnetic field is not homogeneous, most of the previous reports generally assume it to be. In this study, the homogeneity of the magnetic field is improved by adding a third coil between the original two in the conventional Helmholtz coils. In addition, a differential evolution algorithm is used to optimize the parameters for the improved coils by considering a proposed objective function. The results of applying the proposed method are evaluated considering a numerical simulation model of magnetic hyperthermia and compared with the traditional approach using the effective volume percentage of therapeutic temperature (EVPTT) as performance index. The results demonstrate that the uniformity in the magnetic field of the proposed method is significantly better than in the case without considering the proposed optimization. In terms of therapeutic efficiency, the improved magnetic field will translate as better temperature distribution in the tumor region, but the gains are much more evident when the tumor is away from the coil center.

关键词： Magnetic fields Coils Hyperthermia Tumors Temperature distribution Biological system modeling Geometric modeling differential evolution algorithm Helmholtz coils magnetic field magnetic hyperthermia temperature field

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MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期226卷

作者： Kang, Yan Xie, Wentao Wang, Xiaopeng Wang, Haining Wang, Xinchao Li, Jinyuan Yunnan Univ Natl Pilot Sch Software Kunming 650000 Yunnan Peoples R China Kunming Univ Coll Informat Engn Kunming 650217 Yunnan Peoples R China Yunnan Power Grid LLC Co Informat Ctr Kunming 650217 Yunnan Peoples R China

The software module clustering problem (SMCP) aims to improve the internal quality of software while software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP global stability and path complexity as two new objective functions. Specifically, two new concepts are for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures two random individuals as a new community to further improve the search performance. Experiments various projects demonstrate the superiority of the proposed algorithm. The proposed method not only convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods.

关键词： Software modularization Multi-objective optimization differential evolution algorithm Software architecture Software understanding Software system maintenance

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