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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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multi-objective demand fulfillment problem for solar cell industry 46

Multi-objective demand fulfillment problem for solar cell in...

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46th International Conference on Computers and Industrial Engineering (CIE)

作者： Zheng, Jia-Nian Chien, Chen -Fu Wu, Jei-Zheng Asia Univ Dept Business Adm 500 Lioufeng Rd Taichung 41354 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Soochow Univ Dept Business Adm 56 Sect 1Kuei Yang St Taipei 10048 Taiwan

With the growth of green energy market, there are many companies with vertical integration and outsourcing process. Their business models in the solar cell industry cause different influence on the business objectives including revenue, profit, and gross margin. The sales managers are hard to generate fast and reliable ATP (Available-to-promise) which covered all business objectives. Under the various products types with limited quantity, the loading of factory is uncertain and the quantity of order becomes huger. This study formulates multi-objective demand fulfillment problem in solar cell industry and applies a multi-objective genetic algorithm (moGA) to optimize the ATP. The model trade-offs objectives with by Pareto approach without sacrificing other benefits. The multi-population strategy used for stability of solutions. Results of empirical study show that the proposed moGA can improve gross margin while maintaining the desired revenue and profit. With effective solutions and reasonable computing time, the decision support system with proposed moGA has become a regular communication bridge between sales and production departments in the case company.

关键词： Demand fulfillment Solar cell multi-objective genetic algorithm Decision support system

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Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms

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Chinese Physics C 2017年第2期41卷 166-176页

作者：焦毅徐刚 Key Laboratory of Particle Acceleration Physics and Technology Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China

In the lattice design of a diffraction-limited storage ring（DLSR） consisting of compact multi-bend achromats（MBAs）, it is challenging to simultaneously achieve an ultralow emittance and a satisfactory nonlinear performance, due to extremely large nonlinearities and limited tuning ranges of the element parameters. Nevertheless, in this paper we show that the potential of a DLSR design can be explored with a successive and iterative implementation of the multi-objective particle swarm optimization（MOPSO） and multi-objective genetic algorithm（MOGA）. For the High Energy Photon Source, a planned kilometer-scale DLSR, optimizations indicate that it is feasible to attain a natural emittance of about 50 pm·rad, and simultaneously realize a sufficient ring acceptance for on-axis longitudinal injection, by using a hybrid MBA lattice. In particular, this study demonstrates that a rational combination of the MOPSO and MOGA is more effective than either of them alone, in approaching the true global optima of an explorative multi-objective problem with many optimizing variables and local optima.

关键词： diffraction-limited storage ring High Energy Photon Source multi-objective particle swarm optimization multi-objective genetic algorithm lattice design

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Research on a multi-objective genetic algorithm for rational Agent learning model

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International Conference on Information Technology for Manufacturing Systems (ITMS 2011)

作者： Li Ming Xiao Zhenhong Xie Zanfu Mo Xiaoyun Guangdong Polytech Normal Univ Coll Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783037851494

As a software component which is capable of learning in an autonomous way, software agent should have the capability of learning in a dynamic environment. genetic algorithm has a wide perspective in the machine learning because of its unique characteristic (e.g. dynamic adaptability, self-organization, global convergence and robustness). But when applying GA to agent's dynamic learning model, it encounters a series of problem. In this paper, a Modifided multi-objective genetic algorithm(MMOGA) will be introduced to solve these problems. Finally, an Agent's Dynamic learning model based on a MMOGA which has the flexible dynamic learning capability, better global convergence and performance, will be introduced.

关键词： multi-Agent System agent technology multi-objective genetic algorithm Dynamic learning model

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