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A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage

TOP 2016年第1期24卷 206-232页

作者： Karasakal, Esra Silav, Ahmet Middle E Tech Univ Dept Ind Engn TR-06800 Ankara Turkey

In this study, we present a bi-objective facility location model that considers both partial coverage and service to uncovered demands. Due to limited number of facilities to be opened, some of the demand nodes may not be within full or partial coverage distance of a facility. However, a demand node that is not within the coverage distance of a facility should get service from the nearest facility within the shortest possible time. In this model, it is assumed that demand nodes within the predefined distance of opened facilities are fully covered, and after that distance the coverage level decreases linearly. The objectives are defined as the maximization of full and partial coverage, and the minimization of the maximum distance between uncovered demand nodes and their nearest facilities. We develop a new multi-objective genetic algorithm (MOGA) called modified SPEA-II (mSPEA-II). In this method, the fitness function of SPEA-II is modified and the crowding distance of NSGA-II is used. The performance of mSPEA-II is tested on randomly generated problems of different sizes. The results are compared with the solutions of the most well-known MOGAs, NSGA-II and SPEA-II. Computational experiments show that mSPEA-II outperforms both NSGA-II and SPEA-II.

关键词： multi-objective genetic algorithm Facility location Maximal coverage Partial coverage P-center

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multi-objective genetic algorithm based innovative wind farm layout optimization method

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ENERGY CONVERSION AND MANAGEMENT 2015年 105卷 1318-1327页

作者： Chen, Ying Li, Hua He, Bang Wang, Pengcheng Jin, Kai Texas A&M Univ Kingsville Dept Mech & Ind Engn Kingsville TX 78363 USA

Layout optimization has become one of the critical approaches to increase power output and decrease total cost of a wind farm. Previous researches have applied intelligent algorithms to optimizing the wind farm layout. However, those wind conditions used in most of previous research are simplified and not accurate enough to match the real world wind conditions. In this paper, the authors propose an innovative optimization method based on multi-objective genetic algorithm, and test it with real wind condition and commercial wind turbine parameters. Four case studies are conducted to investigate the number of wind turbines needed in the given wind farm. Different cost models are also considered in the case studies. The results clearly demonstrate that the new method is able to optimize the layout of a given wind farm with real commercial data and wind conditions in both regular and irregular shapes, and achieve a better result by selecting different type and hub height wind turbines. Published by Elsevier Ltd.

关键词： Wind farm Layout optimization multi-objective genetic algorithm

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multi-objective genetic algorithm for berth allocation problem considering daytime preference

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 89卷 2-14页

作者： Hu, Zhi-Hua Shanghai Maritime Univ Logist Res Ctr Shanghai 200135 Peoples R China

Maximization of operational efficiency and minimization of cost are pursued by terminal operators, whereas daytime preference is increasingly emphasized by governments, terminal operators and workers. Daytime preference in berth allocation schedule refers to schedule the workloads in nights as fewer as possible, which improves working comfort, safety, and green and energy-savings degrees, but may decrease the throughput and total operational efficiency. By extending existing dynamic discrete berth allocation model, a bi-objective model considering daytime preference is established to minimize the delayed workloads and the workloads in nights. Based on the well known NSGA-II algorithm, a multi-objective genetic algorithm (moGA) is developed for solving the bi-objective model by using a two-part representation scheme. The sensitivities of the algorithmic parameters and tradeoffs between daytime preference and delayed workloads are analyzed by numerical experiments. The algorithmic aspects of the proposed approach and the effects of daytime preference on solutions are all examined. Finally, the managerial implications are discussed. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Berth allocation problem Dynamic discrete berth allocation problem Shiftwork multi-objective genetic algorithm Maritime terminal

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Identification of Medicinal Plant by Fuzzy Local Binary Pattern and multi objective genetic algorithm 5

Identification of Medicinal Plant by Fuzzy Local Binary Patt...

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International Conference on Computer, Control, Informatics and its Applications (IC3INA)

作者： Laxmi, Gibtha Fitri Herdiyeni, Yeni Arkeman, Yandra Ibn Khaldun Univ KH Sholeh Iskandar St Km2 Bogor Indonesia Bogor Agr Univ Bogor Indonesia

ISBN: (纸本)9781538639788

This research proposes multi-objective genetic algorithm non-dominated-sorting (MOGA NSGA-II) of fuzzy local binary pattern to optimize LBP operator and fuzzy threshold for identification of Indonesian medicinal plants. multi-objective genetic algorithm (MOGA) is the genetic algorithm (GA) which is developed specifically for problems with multiple objectives. We evaluated 1,440 medicinal plant leaf images which belong to 30 species. The images were taken from Biofarmaka IPB, Cikabayan Farm, Greenhouse Center Ex-Situ Conservation of Medicinal Plant Indonesia Tropical Forest and Gunung Leutik. FLBP is used to handle uncertainty on images with various patterns. FLBP approach is based on the assumption that a local image neighbourhood may be characterized by more than a single binary pattern. The experimental results show that the correct selection of FLBP operator and threshold using MOGA can reach accuracy of 85%. It can be concluded that this propose method is capable to identify medicinal plants species efficiently and accurately.

关键词： Fuzzy Local Binary Pattern multi-objective genetic algorithm Texture Threshold

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Hydraulic Optimization of a Double-channel Pump's Impeller Based on multi-objective genetic algorithm

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Chinese Journal of Mechanical Engineering 2015年第3期28卷 634-640页

作者： ZHAO Binjuan WANG Yu CHEN Huilong QIU Jing HOU Duohua School of Energy and Power Engineering Jiangsu University Shanghai Kaiquan Pump (Goup) Co. Ltd

Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vort

关键词： double-channel pump＇s impeller multi-objective genetic algorithm artificial neural network computational fluid dynamics（CFD） uni

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Applications of network analysis and multi-objective genetic algorithm for selecting optimal water quality sensor locations in water distribution networks

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第7期19卷 2333-2344页

作者： Yoo, Do Guen Chung, Gunhui Sadollah, Ali Kim, Joong Hoon Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Hoseo Univ Dept Civil Engn Asan 336795 South Korea

In this study, optimal water quality sensor placement is performed based on the sensitivity of flow direction under different water demands for detecting accidental water quality contamination. First, Betweenness Centrality (BC), a network analysis method, is used for determining optimal locations considering a network's connectivity. Second, sensor locations are optimized for minimizing the contaminant intrusion detection time using the travel time matrix and the multi-objective genetic algorithm (MOGA). These methods were applied to two water distribution networks. It was found that the BC method generates optimal locations close to the water sources and the water main, whereas the MOGA-based method generates optimal sensor locations far away from the sources. These results support the following conclusions. First, the installation priority of gauges can be determined with a more objective standard using the aforementioned two methods. Second, given specific objectives, the two models can be used as alternative decision-making tools for sensor installation.

关键词： water distribution system optimized sensors placement sensitivity of flow direction betweenness centrality multi-objective genetic algorithm

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A multi-objective genetic algorithm for determining efficient Risk-Based Inspection programs

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2015年 133卷 253-265页

作者： Moura, Marcio das Chagas Lins, Isis Didier Droguett, Enrique Lopez Soares, Rodrigo Ferreira Pascual, Rodrigo Univ Fed Pernambuco CEERMA Ctr Risk Anal Reliabil & Environm Modeling Recife PE Brazil Univ Maryland Dept Mech Engn Ctr Risk & Reliabil College Pk MD 20742 USA Petrobras SA Rio De Janeiro Brazil Pontificia Univ Catolica Chile Dept Min Engn Phys Asset Management Lab Santiago Chile

This paper proposes a coupling between Risk-Based Inspection (RBI) methodology and multi-objective genetic algorithm (MOGA) for defining efficient inspection programs in terms of inspection costs and risk level, which also comply with restrictions imposed by international standards and/or local government regulations. The proposed RBI+MOGA approach has the following advantages: (i) a user-defined risk target is not required;(ii) it is not necessary to estimate the consequences of failures;(iii) the inspection expenditures become more manageable, which allows assessing the impact of prevention investments on the risk level;(iv) the proposed framework directly provides, as part of the solution, the information on how the inspection budget should be efficiently spent. Then, genetic operators are tailored for solving this problem given the huge size of the search space. The ability of the proposed RBI+MOGA in providing efficient solutions is evaluated by means of two examples, one of them involving an oil and gas separator vessel subject to internal and external corrosion that cause thinning. The obtained results indicate that the proposed genetic operators significantly reduce the search space to be explored and RBI+MOGA is a valuable method to support decisions concerning the mechanical integrity of plant equipment. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Inspection programs Risk reduction Risk-Based Inspection multi-objective genetic algorithm

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Optimal placement of piezoelectric actuators and sensors on a smart beam and a smart plate using multi-objective genetic algorithm

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SMART STRUCTURES AND SYSTEMS 2015年第4期15卷 1041-1062页

作者： Nestorovic, Tamara Trajkov, Miroslav Garmabi, Seyedmehdi Ruhr Univ Bochum Mech Adapt Syst D-44801 Bochum Germany

In this paper a method of fmding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and H-infinity norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.

关键词： optimal location smart structures H-infinity norm genetic algorithm multi-objective genetic algorithm

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Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design

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ENERGY AND BUILDINGS 2015年第Feb.期88卷 135-143页

作者： Yu, Wei Li, Baizhan Jia, Hongyuan Zhang, Ming Wang, Di Chongqing Univ Fac Urban Construct & Environm Engn Chongqing 400045 Peoples R China Chongqing Univ Minist Educ Key Lab Three Gorges Reservoir Reg Ecoenvironm Chongqing 400045 Peoples R China Chongqing Univ Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China State Key Lab Bldg Safety & Built Environm Beijing 100015 Peoples R China

Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA-BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings;Third, the building design multi-objective optimization model was established by using the GA-BP network as a fitness function of the multi-objective genetic algorithm (NSGA-II);Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Building design Energy consumption Thermal comfort Artificial neural network multi-objective genetic algorithm

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A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期77卷 51-66页

作者： Rohaninejad, Mohamad Kheirkhah, Amirsaman Fattahi, Parviz Vahedi-Nouri, Behdin Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

In this paper, a multi-objective flexible job shop scheduling problem with machines capacity constraints is studied. Minimizing the makespan and overtime costs of machines are considered as two objectives for evaluating solutions. First, a new nonlinear integer programming model is presented to formulate the problem. Inasmuch as this problem is well-known as a NP-hard problem, a hybrid metaheuristic algorithm (CFJSP II) is developed to overcome its complexity. Regarding to the solution space of the problem, for assigning and sequencing operations, a multi-objective genetic algorithm based on the ELECTRE method is presented. Also, a powerful heuristic approach to tradeoff the objective functions is developed. Finally, the proposed algorithm is compared with some well-known multi-objective algorithms such as NSGAII, SPEA2, and VEGA. Regarding to the computational results, it is clear that the proposed algorithm has a better performance especially in the closeness of the solutions to the Pareto optimal front.

关键词： Scheduling Flexible job shop multi-objective genetic algorithm ELECTRE method Pareto optimal front

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