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Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9597-9602页

作者： Arkat, Jamal Hosseini, Leila Farahani, Mehdi Hosseinabadi Univ Kurdistan Dept Ind Engn Sanandaj Iran

Cell formation problem is the main issue in designing cellular manufacturing systems. The most important objective in the cell formation problem is to minimize the number of exceptional elements which helps to reduce the number of intercellular movements. Another important but rarely used objective function is to minimize the number of voids inside of the machine cells. This objective function is considered in order to increase the utilization of the machines. We present a bi-objective mathematical model to simultaneously minimize the number of exceptional elements and the number of voids in the part machine incidence matrix. An epsilon-constraint method is then applied to solve the model and to generate the efficient solutions. Because of the NP-hardness of the model, the optimal algorithms can not be used in large-scale problems and therefore, we have also developed a bi-objective genetic algorithm. Some numerical examples are considered to illustrate the performance of the model and the effectiveness of the solution algorithms. The results demonstrate that in comparison with the epsilon-constraint method, the proposed genetic algorithm can obtain efficient solution in a reasonable run time. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cell formation problem Exceptional elements Voids epsilon-constraint method multi-objective genetic algorithm

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Shape Optimization in Product Design Using Interactive genetic algorithm Integrated with multi-objective Optimization 10th

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10th multi-Disciplinary International Workshop on Artificial Intelligence (MIWAI)

作者： Kielarova, Somlak Wannarumon Sansri, Sunisa Naresuan Univ Fac Engn ID3 Ind Design Decis & Dev Res Unit Phitsanulok Thailand Naresuan Univ Fac Engn Dept Ind Engn Phitsanulok Thailand

ISBN: (纸本)9783319493978;9783319493961

This paper proposes an interactive genetic algorithm (IGA) integrated with multi-objective genetic algorithm (MOGA) in development of a generative design system. IGA is used in initializing and handling single dimensionally qualitative objectives. MOGA is used in optimizing two quantitative objectives. Qualitative factors are considered as design objectives to be optimized together with quantitative criteria. The multi-objective optimization is regarded to concurrent handling of two quantitative criteria. Shape of product is modeled by parametric modeling with Rhinoceros and Grasshopper. IGA is processed using Galapagos in Grasshopper. Shape optimization of the product is processed by using MOGA in MATLAB and linked to Grasshopper. Pareto-optimal front is generated to show the optimal solutions, which is able to support designers in decision making. The perfume bottle design is used as an illustration of the proposed framework, but the framework is applicable to other design problems.

关键词： Generative design Interactive genetic algorithm multi-objective genetic algorithm Qualitative multi-objective optimization Pareto-optimal front Bottle design

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Fractional order [PI] Controller and Smith-like Predictor Design for A Class of High Order Systems

Fractional order [PI] Controller and Smith-like Predictor De...

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第30届中国过程控制会议（CPCC 2019）

作者： Zhenlong Wu Jie Yuan Yuquan Chen Donghai Li YangQuan Chen State Key Lab of Power Systems Department of Energy and Power Engineering Tsinghua University School of Automation Southeast University Department of Automation University of Science and Technology of China Mechatronics Embedded Systems and Automation(MESA)Lab School of Engineering University of California

To handle the control difficulties caused by high-order dynamics, a control structure based on fractional order [proportional integral]（PI） controller and fractional order Smith-like predictor for a class of high order systems in the type of K/（Ts+1）~n is proposed in this *** tracking performance and disturbance rejection ability are analyzed based on the terminal value *** shows that the system output can converge to the set point without static error when set point changes and input disturbance *** on the multi-objective genetic algorithm（MO-GA）, simulations under different χ are carried out to analyze the influence on control performance and control *** results show that the control performance can be improved and the energy of the control signal can be reduced simultaneously when the order χ is chosen no more than *** can verify that the fractional order Smith-like predictor with χ<1 has an advantage over the integral order Smith-like predictor with χ=1.

关键词： Fractional order [PI] Controller fractional order Smith-like predictor high order systems multi-objective genetic algorithm

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A growing complex network design method with an adaptive multi-objective genetic algorithm and an inner link restructuring method

A growing complex network design method with an adaptive Mul...

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Society of Instrument and Control Engineers Annual Conference

作者： Haruki Mizuno Takashi Okamoto Seiichi Koakutsu Hironori Hirata Graduate School of Engineering Chiba University

ISBN: (纸本)9781467322591

Many systems that can be modeled using network structures appear in various fields such as informatics, social science, economics, ecology, biology, and engineering. If these systems can be modeled as complex network systems, a complex network design method that finds a desired network structure can become one of strong tools in large-scale system designs. Conventional complex network design methods generally tackle static networks. On the other hand, a growing complex network design method has been proposed. In this method, the network is grown with determining the link structure of the newly-added node in order to obtain a desired network. In this design method, it is expected that characteristics of the optimization problem solved in the algorithm vary along with growth of the network. However, parameters of the optimization method are fixed;hence, its search process may be inefficient. In the design method, connection targets of a new node are only determined, and the structure of the existing network is not changed. Therefore, if the initial network has a large number of nodes, the desired network may not be obtained by using the design method. In this study, we propose to introduce an inner link restructuring method and an adaptive MOGA, that is, an adaptive SPEA2 into the growing complex network design method to deal with foregoing issues. Then, we confirm the effectiveness of the proposed method through numerical experiments.

关键词： Complex Network multi-objective Optimization Growing Network Network Design multi-objective genetic algorithm

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A survivable design of last mile communication networks using multi-objective genetic algorithms

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MEMETIC COMPUTING 2016年第2期8卷 97-108页

作者： Bui, Lam Thu Binh, Huynh Thi Thanh Le Quy Don Tech Univ Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam

In this paper, we are interested in the survivable network design problem (SNDP) for last mile communication networks called (L-SNDP). Given a connected, weighted, undirected graph G = (V, E);a set of infrastructure nodes and a set of customers C including two customer types where customers in the subset C1 require a single connection (type-1) and customers in the subset C2 need to be redundantly connected (type-2). The aim is to seek a subgraph of G with the smallest weight in which all customers are connected to infrastructure nodes and the connections are protected against failures. This is a NP-hard problem and it has been solved only with the objective of minimizing the network cost. In this paper, we introduce a new multi-objective approach to solve L-SNDP called ML-SNDP. These objectives are to minimize the network cost (total cost) and to minimize the maximal amount of sharing links between connections. Results of computational experiments reported show the efficiency of our proposal.

关键词： Survivable network design multi-objective genetic algorithm

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multi-objective Shape Optimization in Generative Design: Art Deco Double Clip Brooch Jewelry Design 7th

Multi-Objective Shape Optimization in Generative Design: Art...

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iCatse International Conference on IT Convergence and Security (ICITCS)

作者： Sansri, Sunisa Kielarova, Somlak Wannarumon Naresuan Univ Fac Engn Dept Ind Engn Phitsanulok Thailand Naresuan Univ Fac Engn Ind Design Decis & Dev Res Unit iD3 Phitsanulok Thailand

ISBN: (纸本)9789811064517;9789811064500

This paper proposes multi-objective optimization generative design (MOOGD) system for generating shapes and optimizing two design objectives. The framework of this paper covers parametric modeling of the product shape configuration using Grasshopper plug-in in Rhinoceros software as well as multi objective optimization developed using Octopus plug-in on Grasshopper. This framework is applied onto the case study of Art Deco double clip brooch jewelry design. The main goals of the study are to design and to optimize shapes of the double clip brooch in two objectives. The first objective is to apply golden ratio to the generating shapes. The second one is to minimize the use of metal referring to weight of the brooch. In the system, MOOGD finally generates a Pareto front to show the optimal solutions, which artists or designers could further use in conceptual product design process. The illustration of the proposed system is provided in this paper.

关键词： Generative design multi-objective genetic algorithm multi objective optimization Pareto-optimal front Art deco double clip brooch

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A NSGA-II based Peak Load Shifting Optimization Method for Customers with Distributed Generators Considering Time-of-use Price 37

A NSGA-II based Peak Load Shifting Optimization Method for C...

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37th Chinese Control Conference (CCC)

作者： Zhou, Hao Ding, Min Hu, Bo Wang, Qingyi Tan, Zhili Li, Danyun China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China State Grid Energy Res Inst Co LTD Beijing 102200 Peoples R China Global Energy Interconnect Dev & Cooperat Org Beijing 100031 Peoples R China

ISBN: (纸本)9789881563958

With the extensive use of distributed generation, the traditional demand response analysis cannot meet the current requirements. This paper proposes a NSGA-II based peak load shifting optimization method for customers with distributed generators considering time-of-use price. Firstly, a fuzzy classification method divides the daily power into three time segments, peak hours, flat hours and valley hours. Secondly, on the basis of the time-of-use price, a peak load shifting optimization model is built with constraints and the objectives of minimizing the peak load, maximizing the valley load, and minimizing the peak-valley difference. Then, a NSGA-II based optimization method solves the optimal model and obtains the optimal electricity prices of different time segments to shift the peak load and the valley load. Finally, the simulation results shows the effectiveness of the proposed method.

关键词： Distributed Generation Demand Respond Time-of-use Price multi-objective genetic algorithm

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Optimal and Feasible Cloud Resource Configurations Generation Method for Genomic Analytics Applications 10

Optimal and Feasible Cloud Resource Configurations Generatio...

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10th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom)

作者： Miura, Katsunori Powell, Courtney Munetomo, Masaharu Otaru Univ Otaru Hokkaido 0478501 Japan Hokkaido Univ Sapporo Hokkaido 0600811 Japan

ISBN: (纸本)9781538678992

This paper proposes a new method that efficiently generates optimal and feasible cloud resource configurations for deploying genomic analytics applications. The proposed method generates optimal and feasible configurations with respect to system requirements by employing two different problem-solving techniques: an equivalent transformation algorithm and a multiobjective genetic algorithm. The equivalent transformation algorithm first generates feasible configurations through computation based on (1) state replacement of clause sets representing various resource configurations and (2) evaluation of their equivalence in terms of declarative meaning to the given requirements. Subsequently, the multi-objective genetic algorithm identifies the optimal configurations with respect to estimated financial cost and computational performance. The input to the proposed method is a logical formula describing the system requirements, whereas the output is a set of unit clauses representing Paretooptimal and feasible cloud resource configurations for deploying a given genomic analytics application. The results of experiments conducted using a sample genomic analytics workflow and Amazon EC2 instances verify the efficacy of the proposed method.

关键词： Cloud resource selection Predicate logic Equivalent transformation algorithm multi-objective genetic algorithm Genomic analytics application

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multi-objective GA for Path Planning of Robot Manipulators Based on Dual Quaternion

Multi-Objective GA for Path Planning of Robot Manipulators B...

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5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Cea-Montufar, C. E. Merchan-Cruz, E. A. Ramirez-Gordillo, J. Vergara-Hernandez, E. Cerro-Ramirez, M. A. Inst Politecn Nacl Hidalgo Unidad Profes Interdisciplinaria Ingn Campus Hidalgo San Agustin Hgo Mexico Inst Politecn Nacl ESIME Unidad Azcapotzalco Secc Estudios Posgrad & Invest Mexico City DF Mexico

ISBN: (纸本)9781538646267

This paper presents a path planning strategy for robotic manipulators based on genetic algorithms and dual quaternions, designing a multi-objective function that allow trajectories be planned in an obstacle-free space and singularity-free kinematic restrictions for robotic manipulators as an optimization problem satisfying position and orientation conditions.

关键词： path planning dual quaternion multi-objective genetic algorithm robot manipulator

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Estimation of Dynamic Passenger Car Unit by multi objective Optimization Technique 8

Estimation of Dynamic Passenger Car Unit by Multi Objective ...

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8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Biswas, Sabyasachi Singh, Somya Malik, Nihal Bisen, Atul, V Amity Univ Dept Civil Engn Amity Sch Engn & Technol Noida India

ISBN: (纸本)9781538617199

Capacity of an intersection is one of the most significant term to assess the efficiency of the urban road network. In order to bring a heterogeneous traffic stream into a homogeneous one consisting of passenger cars only, the concept of Passenger Car Unit (PCU) factor was introduced. From the past decades several methods were proposed by different researchers across the world to estimate the passenger car unit (PCU) in order to convert the heterogeneous traffic into homogeneous traffic. Among all the existing methods, headway ratio method was used frequently for estimating the PCU values at signalised intersections. Though, headway method have some limitation regarding the non-lane discipline behaviour under the highly mix traffic condition. Most of these proposed methods were found to be based on the homogeneous traffic condition. However, different vehicles categories were found to travel in the same traffic stream while in India, traffic condition is found to be highly heterogeneous in nature. Therefore, the proposed methods cannot be implemented directly under highly heterogamous traffic condition. Still, in India, researchers are using static PCU values which can biased the final outcomes of the study. PCU value can vary with the variation in the traffic conditions (i.e. roadway feature, vehicular characteristics and many more). Therefore, there is a need to use the dynamic passenger car unit (DPCU) instead of static one. Present study proposing a new technique for estimating DPCU at signalized intersections based on multi-objective genetic algorithm (MOGA) technique. One of the objective function of this method is to minimize the variance of the saturation flow. Additionally, second objective is to maximize the overall saturation flow in terns of PCU/h. Another aim of the study is to examine the effect of each vehicle types on the PCU value due to their different static and dynamic characteristics which ultimately affect the PCU value.

关键词： Signalized Intersection multi-objective genetic algorithm Optimization Headway

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