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Multi-Objective Optimization of Wavelet-Packet-Based Features in Pathological Diagnosis of Alzheimer Using Spontaneous Speech Signals

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IEEE ACCESS 2020年 8卷 112393-112406页

作者： Nasrolahzadeh, Mahda Haddadnia, Javad Rahnamayan, Shahryar Hakim Sabzevari Univ Dept Biomed Engn Sabzevar *** Iran Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada

Alzheimer's disease (AD) ranks among the main types of neurodegenerative disorders. Patients suffering AD should tackle serious problems since their language skills malfunction. The impact of such disorders is reflected by reduced quality and feature variation of spontaneous speech signals in speech analysis. This paper aims at assessing the variations of some specific types of these energy- and entropy-based features within the frequency range of the speech signals. In the approach followed, the wavelet-packet coefficients are utilized to extract the energy and entropy measures at every spectral sub-band in six successive levels of decomposition. However, the decomposition process conducts a set of high-dimensional feature vectors that is a challenging task for feature selection. This study suggests the application of a non-dominated sorting genetic algorithm-ii (NSGA-ii) for enhancing a group of the sub-band indexes of a wavelet-packet for which the extracted features lead to the highest diagnosis rate of the grouping of Alzheimer's and healthy individuals. The technique proposed here showed that the best overall classification results for both optimized entropy feature vs. energy are more noticeable in discriminating patients with AD from healthy subjects. It is also confirmed the significant impact of multi-objective feature selection on performance of classification (i.e., disease diagnosis) and, its conformity to the disordered nature of the biological signals could help diagnose AD in an efficient manner.

关键词： Alzheimer's disease Feature extraction Wavelet packets Entropy Pathology Alzheimer's disease spontaneous speech signal wavelet packet multi-objective optimization feature selection non-dominated sorting genetic algorithm-ii

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Proposal and Comparative Study of Evolutionary algorithms for Optimum Design of a Gear System

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IEEE ACCESS 2020年 8卷 3482-3497页

作者： Mendez, Maximo Rossit, Daniel A. Gonzalez, Begona Frutos, Mariano Univ Las Palmas de Gran Canaria ULPGC Inst Univ SIANI Las Palmas Gran Canaria 35001 Spain Univ Nacl Sur Dept Engn RA-8000 Bahia Blanca Buenos Aires Argentina INMABB CONICET RA-8000 Bahia Blanca Buenos Aires Argentina IIESS CONICET RA-8002 Bahia Blanca Buenos Aires Argentina

This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the non-dominated sorting genetic algorithm-ii (NSGA-ii). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-ii. The DE-NSGA-ii takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-ii framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.

关键词： Differential evolution evolutionary computation gear train optimization genetic algorithms mechanical engineering multi-objective evolutionary algorithms non-dominated sorting genetic algorithm-ii

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An emergency multi-objective compromise framework for reservoir operation under suddenly injected pollution

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JOURNAL OF HYDROLOGY 2021年 598卷 126242-126242页

作者： Vanda, Sadegh Nikoo, Mohammad Reza Taravatrooy, Narges Sadegh, Mojtaba Al-Wardy, Malik Adamowski, Jan Franklin Shiraz Univ Dept Civil & Environm Engn Shiraz Iran Boise State Univ Dept Civil Engn Boise ID 83725 USA Sultan Qaboos Univ Dept Soils Water & Agr Engn Muscat Oman McGill Univ Dept Bioresource Engn 21111 Lakeshore Rd Ste Anne De Bellevue PQ Canada

An emergency multi-objective framework was developed to achieve an optimal reservoir operating strategy under sudden pollution injection. To assess a wide range of responses to potential future pollution injection events, a large number of reservoir release and pollution injection scenarios (1000 release modes and three injection location scenarios) were considered. The CE-QUAL-W2 model served to simulate pollutant concentration and reservoir cleanup time (RCT) under each scenario. To minimize the reservoir water quality modeling's computational burden, a multilayer perceptron (MLP) neural network was trained and validated against simulated responses to various scenarios. To reduce the dimensions of the problem, forcings of this surrogate model were made orthogonal by principal component analysis (PCA). This surrogate model helps estimate response variables at any intermediate scenario and can be readily coupled with a non-dominated sorting genetic algorithm-ii (NSGA-ii) optimization model to underpin Pareto optimal solutions. Four objective functions were considered: (i) non-supplied water demand, (ii) weighted combination of frequency and magnitude of pollution violation, (iii) ratio of pollution rate released from reservoir outlets to the total rate of injection, and (iv) difference between the reservoir volume and its normal water storage. Finally, a multi-method decision-making procedure was applied to select the best compromise solution for all stakeholders (i.e., Ministry of Energy, Ministry of Agriculture, Center for Environmental Health and Regional Water Authority). This study is the first to propose a reservoir optimal operation model using an MLP-PCA model coupled with several conflict resolution models. Findings showed that the closer the pollution injection point was to the reservoir outlet, the shorter the RCT. Following a 40-day simulation, the solution selected for the injection site closest to the reservoir outlet resulted in a 4-day RCT. Under ide

关键词： Sudden pollution injection Principal component analysis non-dominated sorting genetic algorithm-ii Social choice rules Fallback bargaining

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Manufacturing service supply-demand optimization with dual diversities for industrial internet platforms

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107237-107237页

作者： Hao, Ruiyang Cheng, Ying Zhang, Yongping Tao, Fei Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Industrial internet has been put forward to achieve interconnection between physical world and cyber space towards smart manufacturing, where geographically distributed manufacturing resources are centralized to achieve resource sharing and collaboration among manufacturing enterprises in the form of manufacturing services (MSs). However, MS supply-demand types are diversified and the quantity of MS supply and demand is dynamic, which would pose challenges to MS supply-demand matching and the operational stability of industrial internet platforms. A classified hypernetwork is proposed to depict the dual diversities in both types and quantity variation of MS supply-demand, and the supply-demand ratio of manufacturing services is defined as a main index to reflect the carrying capability of platforms. Then the problem of manufacturing service supply-demand optimization (MS-SDO) is modeled to improve the platforms' operational stability and to satisfy different stakeholders in the platforms, in which the impacts of supply-demand ratio on stakeholders' utility and platforms' operational stability are fully analyzed. Afterwards, a knowledge-guided NSGA-ii algorithm is designed to address the issue of MS-SDO, where traditional genetic procedures are modified to suit our issue and a knowledge-guided search approach is used to improve the search performance. Finally, numerous experiments are carried out for algorithm validation and issue analysis, with a simulation system constructed. Experimental results indicate that the majority of Pareto solutions by our proposed algorithm can dominate the solutions by the traditional algorithm, and some implications are concluded for the platform management and the service integration.

关键词： Supply-demand optimization (SDO) Manufacturing service (MS) Industrial internet Knowledge-guided search non-dominated sorting genetic algorithm-ii (NSGA-ii)

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A Stochastic Sequence-dependent Multi-objective Disassembly Line Balancing Model Subject to Task Failure and Resource Constraint via Multi-objective Cuckoo Search

A Stochastic Sequence-dependent Multi-objective Disassembly ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, TianYuan Guo, XiWang Liu, ShiXin Qi, Liang Zhao, ZiYan Liaoning Shihua Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shandong Univ Sci & Technol Dept Comp Intelligent Sci & Tech Qingdao 266590 Peoples R China

ISBN: (纸本)9781728185262

A Disassembly Line Balancing Problem (DLBP) exists in the remanufacturing of discarded products. It involves such factors as sequence-dependent among components, multi-resource constraints, limited number of workstations, uncertainty of disassembly time, and disassembly failure risk. Effective decisions can be made by taking them into full consideration. This work establishes a stochastic sequence-dependent multi-objective DLBP model subject to disassembly failure and resource constraints. Its objectives are maximization of profit and minimization of energy consumption. A multi-objective cuckoo search algorithm is proposed. Then, three real products are disassembled to verify the effectiveness and feasibility of the proposed approach. Experimental results show the superior of the proposed algorithm over multi-objective artificial bee colony algorithm and non-dominated sorting genetic algorithm ii.

关键词： Disassembly line balancing resource-constrained Metaheuristic algorithms Cuckoo Search Artificial Bee Colony non-dominated sorting genetic algorithm-ii

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A practical multi-objective optimization sectorization method for water distribution network

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 656卷 1401-1412页

作者： Zhang, Kui Yan, Hexiang Zeng, Han Xin, Kunlun Tao, Tao Tongji Univ Coll Environm Sci & Engn Shanghai Inst Pollut Control & Ecol Secur Siping Rd Shanghai 200092 Peoples R China UN Environm Tongji Inst Environm Sustainable Dev Siping Rd Shanghai 200092 Peoples R China

In recent years, water leakage problems in water distribution networks (WDN) have been attracting more attention, with an emphasis on energy and water resources. As one of the measures used for flow monitoring and leakage control, water network sectorization is a research hotspot in China. This paper, which begins with an introduction of present sectorization methods, proposes a multi-objective optimization sectorization method based on a comprehensive consideration of the hydraulics, water quality and economy. This method is based on the non-dominated sorting genetic algorithm (NSGA-ii) which is a heuristic algorithm formulti-objective optimization to obtain the optimal schemes. In addition, human experience is also considered in the optimization process. In a case study, this method proves to be efficient in producing good results with little impact on the hydraulics andwater quality of theWDN, and the results obtained are acceptable for multiple objectives. Therefore, this method provides references for the transformation of future water distribution network sectorization. (C) 2018 Elsevier B. V. All rights reserved.

关键词： Water distribution network sectorization District metering area Multi-objective optimization method non-dominated sorting genetic algorithm-ii

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Multi-Objective Optimization for Multi-Line and U-Shape Layout of Sewing Assembly Line in Apparel Industry

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IEEE ACCESS 2019年 7卷 107188-107200页

作者： Zhang, Xujing Wang, Lichuan Chen, Yan Soochow Univ Coll Text & Clothing Engn Suzhou 215021 Peoples R China

The design of assembly line layout is one of the most important influential factors for the company's performance. The inappropriate arrangement of the order of workstations in the assembly line could cause the excessive movement of the material. In order to increase productivity and reduce production cost, a multi-objective optimization model with minimum the transfer distance of semi-finished products and the total area of assembly line was established in this paper. The non-dominated sorting genetic algorithm-ii was applied to obtain the results of this model. The coding method, genetic operation and fitness function for three different kinds of sewing assembly line layouts (multi-line, U-shape two-line and U-shape three-line) were studied, while the workstations were organized in three ways: the order of processes, the type of machines, and the components of garment. The efficiency of the model was verified by the practice of sewing assembly lines for men's shirt. The layout schemes are compared with other algorithms. The results illustrated that NSGA-ii algorithm is an effective tool to solve the sewing assembly line layout problem. The transfer distances of semi-finished products in the multi-line and U-shape (three-line) layouts arranged by garment components workstation layout were short. The multi-line layout arranged by the type of machines workstation layout occupied the smallest area. The model is suitable for solving the layout problem of the garment sewing workshop in practice, shortening the production cycle, and reducing the production cost. A variety of assembly line layout schemes are provided for apparel manufacturers in this paper. Apparel manufacturers can select the appropriate assembly line layout based on the actual conditions of the workshop and product.

关键词： Sewing assembly line layout workstation layout multi-objective optimization non-dominated sorting genetic algorithm-ii

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Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network

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NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 4945-4962页

作者： Ibrahim, Ashraf Osman Shamsuddin, Siti Mariyam Abraham, Ajith Qasem, Sultan Noman Alzaiem Alazhari Univ Fac Comp Sci & Informat Technol Khartoum 13311 Sudan Arab Open Univ Khartoum Sudan Future Univ Fac Comp Sci Khartoum Sudan Univ Teknol Malaysia UTM Big Data Ctr Skudai 81310 Johor Malaysia MIR Labs Sci Network Innovat & Res Excellence Washington DC USA IMSIU Coll Comp & Informat Sci Comp Sci Dept Riyadh Saudi Arabia Taiz Univ Fac Appl Sci Comp Sci Dept Taizi Yemen

In recent years, multi-objective evolutionary optimization algorithms have shown success in different areas of research. Due to their efficiency and power, many researchers have concentrated on adapting evolutionary algorithms to generate Pareto solutions. This paper proposes a new memetic adaptive multi-objective evolutionary algorithm that is based on a three-term backpropagation network (MAMOT). This algorithm is an automatic search method for optimizing the parameters and performance of neural networks, and it relies on the use of the adaptive non-dominated sorting genetic algorithm-ii integrated with the backpropagation algorithm, being used as a local search method. The presented MAMOT employs a self-adaptive mechanism toward improving the performance of the proposed algorithm and a local optimizer improving all the individuals in a population in order to obtain better accuracy and connection weights. In addition, it selects an appropriate number of hidden nodes simultaneously. The proposed method was applied to 11 datasets representing pattern classification problems, including two-class, multi-class and complex data reflecting real problems. Experiments were performed, and the results indicated that the proposed method is viable in pattern classification tasks compared to a multi-objective genetic algorithm based on a three-term backpropagation network (MOGAT) and some of the methods mentioned in the literature. The statistical analysis results of the t test and Wilcoxon signed-ranks test also show that the performance of the proposed method is significantly better than MOGAT.

关键词： Self-adaptive Artificial neural networks Three-term backpropagation Multi-objective evolutionary non-dominated sorting genetic algorithm-ii

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Portfolio selection of distributed energy generation projects considering uncertainty and project interaction under different enterprise strategic scenarios

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APPLIED ENERGY 2019年 236卷 444-464页

作者： Wu, Yunna Xu, Chuanbo Ke, Yiming Li, Xinying Li, Lingwenying North China Elect Power Univ Sch Econ & Management Beijing Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Beijing 102206 Peoples R China

Selecting a rational distributed energy generation (DEG) project portfolio is the key to achieving the strategic objectives of energy enterprises. The complexity associated with the selection of DEG project portfolio comes from uncertainties in decision-making environment, interactions between projects, and necessary alignment with the strategic objectives of enterprises. However, previous researches did not address the three issues simultaneously. To fill such gap, this study establishes a multi-criteria decision-making framework to select the optimal DEG project portfolio(s) under different strategic scenarios, where uncertainty and project interaction are considered. The framework consists of two stages. In the first phase, the weights of criteria are determined by the interval type-2 fuzzy analytic hierarchy process technique, and the strategic alignment indexes of each candidate distributed energy generation project are obtained using the interval type-2 fuzzy weighted averaging operator. In the second stage, considering the strategic interactions, a nonlinear 0-1 programming is formulated while satisfying the budget constraints, and the non-dominated sorting genetic algorithm-ii is utilized to obtain the optimal portfolio of DEG projects under different strategic scenarios. The proposed framework is applied in a case study to illustrate its suitability and effectiveness. The results show that the selected portfolios vary with the strategic objectives of enterprises. This research has practical applied value for project managers in project portfolio management.

关键词： Distributed energy source Project portfolio Multi-criteria decision-making Interval type-2 fuzzy analytic hierarchy process Interval type-2 fuzzy weighted averaging non-dominated sorting genetic algorithm-ii

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Multi-objective exergoeconomic and exergoenvironmental optimization of continuous synthesis of solketal through glycerol ketalization with acetone in the presence of ethanol as co-solvent

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RENEWABLE ENERGY 2019年第Jan.期130卷 735-748页

作者： Aghbashlo, Mortaza Hosseinpour, Soleiman Tabatabaei, Meisam Rastegari, Hajar Ghaziaskar, Hassan S. Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran AREEO ABRII Microbial Biotechnol Dept POB 31535-1897 Karaj Iran Biofuel Res Team BRTeam Karaj Iran Isfahan Univ Technol Dept Chem Esfahan Iran

This work was aimed at conducting a multi-objective exergoeconomic and exergoenvironmental optimization of continuous synthesis of solketal through glycerol ketalization with acetone in the presence of ethanol as co-solvent and Purolite PD206 as catalyst. Exergoeconomic and exergoenvironmental performance parameters of the reactor were computed and discussed comprehensively after writing and solving their balance equations based on the experimental data. The effects of process parameters viz. ketalization temperature (T), acetone/glycerol molar ratio (X), feed flow rate (F), reaction pressure (P), and catalyst quantity (C) on the exergy-based variables were investigated in detail. The optimization process was performed based on minimizing two more important exergetic parameters, i.e., cost and environmental per unit of exergy for the product. To this end, an elaborated coupled version of adaptive neuro-fuzzy inference system (ANFIS) and non-dominated sorting genetic algorithm-ii (NSGA-ii) was employed. The ANFIS approach was used for modeling the process, while the NSGA-ii was applied for finding the optimum operating conditions of the reactor. According to the results obtained, the ANFIS approach successfully predicted both objective parameters with an R-2 higher than 0.99. The optimum ketalization conditions for solketal synthesis in the developed reactor corresponded to T = 35.1 degrees C, X = 4.5, F = 0.4 mL/min, P = 26.7 bar, and C = 2.2 g, leading to the cost and environmental impact per unit of exergy for the product of 5032.9 USD/GJ and 143.9 mPts/GJ, respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Adaptive neuro-fuzzy inference system Continuous glycerol ketalization Cost and environmental per unit of exergy for the product Exergoeconomic and exergoenvironmental analyses non-dominated sorting genetic algorithm-ii Solketal synthesis

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