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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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Developing an integrated model for planning the delivery of construction materials to post-disaster reconstruction projects

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第3期9卷 1135-1156页

作者： Gharib, Zahra Yazdani, Maziar Bozorgi-Amiri, Ali Tavakkoli-Moghaddam, Reza Taghipourian, Mohammad Javad Univ Tehran Coll Engn Sch Ind Engn Tehran *** Iran Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia Islamic Azad Univ Dept Business Management Chalous Branch Chalous 46615397 Iran

Construction material delivery to post-disaster reconstruction projects is challenging because of the resource and time limitations that follow a large-scale disaster. There is compelling evidence that inadequate planning jeopardises the success of a large number of post-disaster reconstruction projects. Thus, the current study proposes an integrated approach to facilitate the procurement planning of construction materials following a large-scale disaster. The proposed approach clustered the location of construction projects using a differential evolution (DE)-K-prototypes, a new partitional clustering algorithm based on DE and K-prototypes, method. Then, using a permanent matrix prioritises cluster points based on route reliability-affecting factors. The model's objectives are to minimise the total travel time, maximise the reliability of the route, and minimise the total weighted undelivered materials to projects. In the case of distribution of material through land vehicles, the possibility of breakdowns in the vehicle is considered, allowing for the determination of vehicle breakdown under various scenarios and the minimisation of undelivered materials to projects. As a result of the uncertain character of the disaster, the demands of construction projects are fuzzy, and Jimenez's method is used to handle it. Due to the complexity of the problem, two algorithms are proposed, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) and a non-dominated sorting genetic algorithm-ii (NSGA-ii). The results confirm that the proposed MOEA/D has a higher accuracy while NSGA-ii has a shorter computational time. By providing new theoretical perspectives on disaster recovery strategies in the construction sector, this study contributes to the growing body of knowledge about disaster recovery strategies in the sector. The findings of this study can be employed to develop an integrated planning system for the delivery of construction materials to post-disaste

关键词： post-disaster reconstruction non-dominated sorting genetic algorithm-ii post disaster construction material multi-objective evolutionary algorithm based on decomposition augmented epsilon-constraint method

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A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm

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INFORMATION SCIENCES 2017年 402卷 124-148页

作者： Ozkis, Ahmet Babalik, Ahmet Selcuk Univ Fac Engn Dept Comp Engn TR-42075 Konya Turkey

This study investigates a multi-objective Vortex Search algorithm (MOVS) by modifying the single-objective Vortex Search algorithm or VS. The VS is a metaheuristic-based algorithm that uses a new adaptive step-size adjustment strategy to improve the performance of the search process. Search mechanism of the VS is inspired by the vortex pattern, so it is called a "Vortex Search" algorithm. The original VS is an improved way of solving single-objective continuous problems. To improve the MOVS algorithm, the VS algorithm is enhanced with added calculation approaches, such as fast-nondominated-sorting and crowding-distance, in order to identify the degree of non-dominance of the solutions and the densities of their occurrence. In addition, a crossover operation is added to the MOVS algorithm in order to enhance the Pareto front convergence capacity of the solutions. Finally, to spread the solutions more successfully over the Pareto front, it has been randomly produced using the inverse incomplete gamma function using a parameter between 0 and 1. The proposed MOVS algorithm is tested against 36 different benchmark problems together with NSGAii, MOCeii, IBEA and MOEA/D algorithms. The test results indicate that the MOVS algorithm achieves a better performance on accuracy and convergence speed than any other algorithms when comparisons are made against several test problems, and they also show that it is a competitive algorithm. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Vortex search algorithm Multi-objective optimization Metaheuristics non-dominated sorting genetic algorithm-ii

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Design optimisation of railway pantograph-catenary systems with multiple objectives

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VEHICLE SYSTEM DYNAMICS 2023年第11期61卷 2953-2975页

作者： Wang, Hanlei Zheng, Dingyang Huang, Pu Yan, Wenyi Monash Univ Dept Mech & Aerosp Engn Clayton Australia Monash Univ Inst Railway Technol Clayton Australia

Both the mean contact force difference and the standard deviation of the contact force were minimised in the presented optimised design of a pantograph-catenary system for the purposes of improving of the current collection quality and the reduction of the contact wear. The non-dominated sorting genetic algorithm-ii (NSGA-ii) was applied to optimise the pantograph-catenary interaction system. The adopted NSGA-ii algorithm was improved by avoiding repetition of FEM simulations with the duplicated input parameters and retaining only one of the duplicated results for the next generation selection. Either the catenary or the pantograph can be optimised individually by using the proposed versatile approach. In this research, the design optimisation of the catenary system was conducted first. After that, the pantograph was further optimised based on the optimised design of the catenary system. A case study indicated that the standard deviation of the contact force was reduced by 33.4% in the optimised catenary design and 39% in the optimised pantograph design, and the contact force differences were reduced by 98.3% and 99.9% in the optimised catenary design and the optimised pantograph design, respectively.

关键词： Railway catenary pantograph-catenary interaction optimisation multi-objective non-dominated sorting genetic algorithm-ii

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Optimization strategy for SAM in nuclear power plants based on NSGA-ii

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KERNTECHNIK 2023年第6期88卷 710-724页

作者： Zhou, Sikai Xie, Mingliang Zheng, Jianxiang Miao, Huifang Xiamen Univ Coll Energy 4221-104 Xiangan South Rd Xiamen 361002 Peoples R China Fujian Res Ctr Nucl Engn Xiamen 361102 Fujian Peoples R China Naval Univ Engn Coll Nucl Sci & Technol Wuhan 430033 Hubei Peoples R China China Nucl Power Operat Technol Corp Wuhan 430223 Hubei Peoples R China

The Severe Accident Management Guide (SAMG) is an important component of nuclear safety regulations. Many studies are being conducted to optimize severe accident management (SAM) strategies. To ensure the safety of nuclear power plants, decision makers need to monitor multiple parameters with security threats. Therefore, it is particularly important to search optimal SAM strategies under different numbers of mitigation targets. The non-dominated sorting genetic algorithm-ii (NSGA-ii) is an evolutionary algorithm that does not require derivative differentiation and is capable of population search. In this study, a nuclear power plant accident optimization strategy is developed using the Modular Accident Analysis Program (MAAP) in conjunction with NSGA-ii. The strategy enables decision makers to consider multiple mitigation objectives in a complex decision environment. Focusing on the CPR1000, this study applies the optimization strategy to automatically search for optimal mitigation strategies for small break loss of coolant accident (SBLOCA) and station blackout hot leg creep rupture accidents (SBOHLCR). Comparing the optimization results with the basic accident sequence, it is found that the reactor pressure vessel (RPV) failure time is delayed from 72,702 s to 128,730 s under SBLOCA and from 23,828 s to 28,363 s under SBOHLCR. This study has also verified that the optimal SAM strategy obtained by the strategy through dual objective optimization has better mitigation effects than a strategy that only considers one objective. This optimization strategy has the potential to be applied to other types of severe accident management studies in the future.

关键词： modular accident analysis program multi-objective optimization non-dominated sorting genetic algorithm-ii nuclear power optimization strategy

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Bi-objective optimization using an improved NSGA-ii for energy-efficient scheduling of a distributed assembly blocking flowshop

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ENGINEERING OPTIMIZATION 2023年第5期55卷 719-740页

作者： Niu, Wei Li, Jun-qing Jin, Hui Qi, Rui Sang, Hong-yan Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Liaocheng Univ Sch Comp Liaocheng Shandong Peoples R China

In this study, an Energy-Efficient Distributed Assembly Blocking FlowShoP (EEDABFSP) is considered. An improved non-dominated sorting genetic algorithm-ii (NSGA-ii) is developed to solve it. Two objectives have been considered, i.e. minimizing the maximum completion time and total energy consumption. To begin, each feasible solution is encoded as a one-dimensional vector with the factory assignment, operation scheduling and speed setting assigned. Next, two initialization schemes are presented to improve both quality and diversity, which are based on distributed assembly attributes and the slowest allowable speed criterion, respectively. Then, to accelerate the convergence process, a novel Pareto-based crossover operator is designed. Because the populations have different initialization strategies, four different mutation operators are designed. In addition, a distributed local search is integrated to improve exploitation abilities. Finally, the experimental results demonstrate that the proposed algorithm is more efficient and effective for solving the EEDABFSP.

关键词： Distributed permutation blocking flowshop energy-efficient non-dominated sorting genetic algorithm-ii multi-objective

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Optimization of a multi-objective lean assembly problem with reconfigurable machine cells

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第6-7期237卷 1060-1071页

作者： Pattanaik, Laxmi Narayan Kant, Rajeev Birla Inst Technol Dept Prod & Ind Engn Ranchi 835215 Bihar India Guru Gobind Singh Educ Soc Tech Campus Dept Mech Engn Bokaro India

Manufacturing sector is regularly facing the challenges caused due to high market dynamics and mass customization. Traditionally designed machine cells fail to address this issue due to lack of flexibility in capacity and functionality. However, the benefits of cell based production can be achieved by changing the machines involved and the design of cells. This paper presents a hybrid model of machine cells comprising of reconfigurable machine tools (RMTs) that are acting as part feeders for a lean assembly line of discrete products. The strategies of lean manufacturing to maintain the Takt time and synchronized one-piece-flow are considered in the model. A multi-objective optimization problem is formulated and solved to minimize the inter-cellular part movement, the error for Takt time among machine cells and the total reconfiguration time of the RMTs using NSGA-ii metaheuristic. A numerical case example for the model is solved using MATLAB (c) and illustrated along with computational steps and Pareto optimal solutions.

关键词： Cellular manufacturing system reconfigurable machine tools lean manufacturing Takt time non-dominated sorting genetic algorithm-ii

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Multi-objective prediction and optimization of performance of three-layer latent heat storage unit based on intermittent charging and discharging strategies

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RENEWABLE ENERGY 2024年 225卷

作者： Zhang, Chenyu Ma, Zhenjun Qu, Zhiguo Xu, Hongtao Univ Shanghai Sci & Technol Sch Energy & Power Engn 516 Jungong Rd Shanghai 200093 Peoples R China Univ Wollongong Sustainable Bldg Res Ctr Wollongong NSW 2522 Australia Xi An Jiao Tong Univ Sch Energy & Power Engn MOE Key Lab Thermo Fluid Sci & Engn Xian 710049 Shaanxi Peoples R China

An intermittent heat charging and discharging strategy is proposed for on -demand thermal utilization in a threelayer latent heat storage unit filled with nanoparticle-enhanced phase change materials. To optimize the utilization ratio of phase change materials, and the stored and released thermal exergy amounts, a multi -objective prediction and optimization methodology combining orthogonal experimental design, range and variance analyses, multi -nonlinear regression models, and non -dominated sorting genetic algorithm -ii is introduced while considering the variables of nanoparticle concentration, heat transfer fluid velocity, and intermittent time interval. Results show that the time interval presents the most significant influence. Multi -nonlinear regression models for the above three variables are established with determination factors of 0.9871, 0.9625, and 0.9253, respectively. The ultimate optimal results are 0.8, 57094.03 J, and 43066.73 J, achieved at the three variables of 44.37 min, 0.38 m s -1 and 8.99%, respectively. The maximum verification error of 5.11% indicates the reliability of this methodology. The methodology aims to enhance the overall performance of the three -layer latent heat storage system by mitigating the constraints associated with single -performance optimization.

关键词： Latent heat storage Orthogonal experimental design Analysis of variance Multi-nonlinear regression 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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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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