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A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

COMPUTERS & INDUSTRIAL ENGINEERING 2007年第4期53卷 642-666页

作者： Rahimi-Vahed, Alireza Mirzaei, Alil Hossein Univ Tehran Dept Ind Engn Tehran Iran Tarbiat Modares Univ Dept Ind Engn Tehran Iran

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers' demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： mixed-model assembly line multi-objective sequencing problem multi-objective shuffled frog-leaping algorithm bacterial optimization multi-objective genetic algorithm

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Optimal sensor placement for event detection and source identification in water distribution networks

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JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA 2014年第1期63卷 51-57页

作者： Liu, Shuming Auckenthaler, Pierre Tsinghua Univ Sch Environm Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Univ VEOLIA Environm Joint Res Ctr Adv E Beijing 100084 Peoples R China

This study focuses on the optimization of sensor placement with respect to the source identification and event detection. A multi-objective algorithm is used to solve the optimization problem. The numbers of possible source nodes for contamination events associated with the solutions on the Pareto fronts from the proposed method and benchmark method are calculated under the same configuration and compared. The comparison showed that the proposed method performs better than the benchmark method in detecting a contamination event and identifying its possible source.

关键词： multi-objective genetic algorithm nodal demand uncertainties sensor placement source identification

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A TSP-GA multi-objective algorithm for flow-shop scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2004年第11-12期23卷 909-915页

作者： Ponnambalam, SG Jagannathan, H Kataria, M Gadicherla, A Monash Univ Malaysia Sch Engn Sci Petaling Jaya 46150 Selangor Malaysia Reg Engn Coll Dept Prod Engn Tiruchirappalli 620015 India

A multi-objective evolutionary search algorithm using a travelling salesman algorithm and genetic algorithm for flow-shop scheduling is proposed in this paper. The initial sequence is obtained by solving the TSP. The initial population of the genetic algorithm is created with the help of a neighbourhood creation scheme known as a random insertion perturbation scheme, which uses the sequence obtained from TSP. The proposed algorithm uses a weighted sum of multiple objectives as a fitness function. The weights are randomly generated for each generation to enable a multi-directional search. The performance measures considered include minimising makespan, mean flow time and machine idle time. The performance of the proposed algorithm is demonstrated by applying it to benchmark problems available in the OR-Library.

关键词： flow-shop scheduling multi-objective genetic algorithm

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A Markov chain approach for double-objective economic statistical design of the variable sampling interval (X)over-bar control chart

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2018年第2期47卷 277-288页

作者： Safe, H. Kazemzadeh, R. B. Kanani, Y. Gholipour Islamic Azad Univ Qaemshahr Branch Dept Ind Engn Qaemshahr Iran Tarbiat Modares Univ Fac Ind & Syst Engn Tehran *** Iran

An (X) over bar chart with variable sampling interval (VSI) has been shown superior to the traditional (X) over bar chart with fixed ratio sampling (FRS). A constraint approach is not an efficient method for Economic Statistical Design (ESD) of VSI control charts because statistical properties are of the same importance as economic properties and should be optimized simultaneously. Then, a multi-objective genetic algorithm for ESD is proposed for identifying the Pareto optimal solutions of control chart design. The proposed method allows the practitioner to be provided with a set of optimal designs rather than a single solution, and they can select the locally optimal solution according to the process information. Through an illustrative example, the advantages of the proposed approach are shown by providing a list of viable optimal solutions and graphical representations, which indicate the advantage of flexibility and adaptability of our approach. Performance measures of these adaptive control charts are obtained through a Markov chain approach.

关键词： Economic statistical design Markov chain multi-objective genetic algorithm Pareto front Variable sampling interval (VSI)

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Optimization investigation on configuration parameters of serrated fin in plate-fin heat exchanger based on fluid structure interaction analysis

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年 119卷 282-294页

作者： Wen, Jian Li, Ke Zhang, Xing Wang, Chunlong Wang, Simin Tu, Jiyuan Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Dept Proc Equipment & Control Engn Xian 710049 Shaanxi Peoples R China Tsinghua Univ Inst Nucl & New Energy Technol Key Lab Adv Reactor Engn & Safety Minist Educ Beijing 100084 Peoples R China

The comprehensive performance of serrated fin in plate-fin heat exchangers (PFHEs) is numerically studied based on fluid structure interaction (FSI) analysis in this paper. Based on Full 2nd-Order Polynomial response surface (RS) and analysis of variance (ANOVA), the effects and second order interaction effects of the fin height, fin space, fin thickness and fin interrupted length on heat transfer, flow resistance and stress are quantitatively and thoroughly assessed. The results show that the heat transfer is the most sensitive to fin interrupted length, and the flow resistance and the maximum stress are the most sensitive to fin thickness. The interaction effect of the fin space and fin interrupted length is the strongest when the j factor is set as objective function, while the interaction effect of the fin space and fin thickness is the strongest when the f factor or maximum stress is set as objective function. Based on Full 2nd-Order Polynomial RS, multi-objective genetic algorithm (MOGA) is applied to optimize the fin structure comprehensively, with enhancing heat transfer, decreasing pressure drop and stress set as objectives. To demonstrate the effectiveness of optimized structures, a comprasion between the original design and optimized structures is performed. The results show that the JF factor of optimized structures 2 and 3 increases by 23.0% and 19.7% respectively, and the maximum stress decreases by 5.8% and 15.2% respectively. The MOGA offers theorectical guidance for optimization design of PFHE. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Plate-fin heat exchanger Serrated fin Stress analysis Response surface Analysis of variance multi-objective genetic algorithm

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Protein secondary structure prediction using DWKF based on SVR-NSGAII

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NEUROCOMPUTING 2012年 94卷 87-101页

作者： Zangooei, Mohammad Hossein Jalili, Saeed Tarbiat Modares Univ Elect & Comp Engn Fac Dept Comp Engn SCS LabSch Elect & Comp Engn Tehran Iran

Prediction of protein secondary structure is an important step towards elucidating its three dimensional structure and its function. This is a challenging problem in bioinformatics. By introduction of machine learning for protein structure prediction, a solution has brought to this challenge to some extent. In the literature of Machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variable. There are endeavors to use regression methods to solve the classification problem and vice versa. To regard a classification problem as a regression one, we proposed a method which is based on Support Vector Regression (SVR) classification model as one of the powerful methods in the field of machine intelligence. We applied non-dominated Sorting genetic algorithm II (NSGAII) to find mapping points (MPs) for rounding a real-value to an integer one. Also NSGAII is used for finding out and tuning SVR kernel parameters optimally to enhance the performance of our model and achieve better results. At the other hand, using a suitable SVR kernel function for a particular problem can improve the prediction results remarkably but there is not a kernel which can predict all protein secondary structure classes with acceptable accuracy. Therefore we use a Dynamic Weighted Kernel Fusion (DWKF) method for fusing of three SVR kernels to achieve a supreme performance. Also to improve our method, Position Scoring Matrix (PSSM) profiles are used as the input information to it. The goals of this research are to regulate SVR parameters and fuse different SVR kernel outputs in order to determine protein secondary structure classes accurately. The obtained classification accuracies of our method are 85.79% and 84.94% on RS126 and CB513 datasets respectively and they are promising with regard to other classification methods in the literature. Moreover, for gauging our method behavior in

关键词： Protein secondary structure prediction Machine learning approach Support vector regression multi-objective genetic algorithm

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GA-fuzzy control of smart base isolated benchmark building using supervisory control technique

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ADVANCES IN ENGINEERING SOFTWARE 2007年第7期38卷 453-465页

作者： Kim, Hyun-Su Roschke, Paul N. Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Sungkyunkwan Univ Dept Architectural Engn Suwon South Korea

The effectiveness of a supervisory fuzzy control technique for reduction of seismic response of a smart base isolation system is investigated in this study. To this end, a first generation, base isolated, benchmark building is employed for numerical simulation. The benchmark structure under consideration has eight stories and an irregular plan. Furthermore it is equipped with low damping elastomeric bearings and magnetorheological (MR) dampers for seismic protection. The proposed control technique employs a hierarchical structure of fuzzy logic controllers (FLC) consisting of two lower-level controllers (sub-FLC) and a higher-level supervisory controller. One sub-FLC has been optimized for near-fault earthquakes and the other sub-FLC is well-suited for far-fault earthquakes. These sub-FLCs are optimized by use of a multi-objective genetic algorithm. Four objectives, i.e. reduction of peak superstructure acceleration, peak isolation system deformation, RMS superstructure acceleration and RMS isolation system deformation are used in a multi-objective optimization process. When an earthquake is applied to the benchmark building, each of the sub-FLCs provides different command voltages for the semi-active controllers and the supervisory fuzzy controller appropriately combines the two command voltages based on a fuzzy inference system in real time. Results from numerical simulations demonstrate that isolation system deformation as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique in comparison with a sample clipped optimal controller. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： smart base isolation benchmark control problem supervisory fuzzy control multi-objective genetic algorithm MR damper

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A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem

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SOFT COMPUTING 2007年第10期11卷 997-1012页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Rabbani, M. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran

The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.

关键词： mixed-model assembly line multi-objective sequencing problem just-in-time multi-objective particle swarm multi-objective genetic algorithm

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Prediction of building's temperature using neural networks models

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ENERGY AND BUILDINGS 2006年第6期38卷 682-694页

作者： Ruano, AE Crispim, EM Conceiçao, EZE Lúcio, MMJR Univ Algarve Fac Sci & Technol Ctr Intelligent Syst P-8005139 Faro Portugal Univ Algarve Fac Marine & Environm Sci P-8005139 Faro Portugal Direcc Reg Educ Algarve Sitio Figuras P-8000761 Faro Portugal

The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. In this paper the design of inside air temperature predictive neural network models, to be used for predictive control of air-conditioned systems, is discussed. The use of multi-objective genetic algorithms for designing off-line radial basis function neural network models is detailed. The performance of these data-driven models is compared, favourably, with a multi-node physically based model. Climate and environmental data from a secondary school located in the south of Portugal, collected by a remote data acquisition system, are used to generate the models. By using a sliding window adaptive methodology, the good results obtained off-line are extended throughout the whole year. The use of long-range predictive models for air-conditioning systems control is demonstrated, in simulations, achieving a good temperature regulation with important energy savings. (C) 2005 Elsevier B.V. All rights reserved.

关键词： temperature prediction neural networks multi-objective genetic algorithm radial basis function networks

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An integrated optimum 3D transition orbit design procedure with guidance for a spacecraft

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ADVANCES IN SPACE RESEARCH 2021年第7期68卷 2742-2751页

作者： Zardashti, R. Malek Ashtar Univ Technol Fac Aerosp Shahin Shahr Iran

In this paper, an integrated optimal flight plan with a guidance plan in a general transfer orbit is considered. Due to the need to simultaneously reduce energy consumption and flight time in some space missions, the spacecraft trajectory is optimized based on two criteria to minimize fuel consumption and flight time in the orbital transfer process. For this purpose, the total velocity impulse on both sides of the transition orbit and the elapsed time are defined as the criteria of the objective function which includes the spacecraft's insertion conditions and orbital parameters related to the shape of the trajectory. The optimal transition (coasting) trajectory is then obtained using the multi-objective genetic algorithm (MOGA) search method. Subsequently, the velocity-to-be-gained guidance steering scheme is integrated to track the trajectory onboard and reach the final orbit. The numerical results of the proposed algorithm are extracted in three case studies and compared with other references that show its ability to find the optimal transfer. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Spacecraft trajectory Transfer orbit optimization multi-objective genetic algorithm Rendezvous Targeting problem

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