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3rd IEEE International Conference on Network Infrastructure and Digital Content. (IC-NIDC)

作者： Liu, Xiaoxu Gu, Rentao Ji, Yuefeng Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Sch Informat & Commun Engn Beijing 100876 Peoples R China

ISBN: (纸本)9781467322010;9781467322034

This paper researches and formulates a topology optimization model in long-reach passive optical network (LRPON). The objective is to minimize the deployment cost with certain physical constraints. A nested genetic algorithm (NGA) is proposed to solve the topology optimization problem. The NGA consists of two loops, and the outer-loop GA (OLG) is a simple genetic algorithm (SGA) to solve the location problem of splitters, while the inner-loop GA (ILG) is a partheno-genetic algorithm (PGA) to complete the construction of the spanning tree. Additionally, three scenarios with different network scales are simulated to examine the efficiency of the proposed algorithm.

关键词： nested genetic algorithm LRPON Topology optimization

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Optimization of Uncertain Structures with Interval Parameters Considering Objective and Feasibility Robustness

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Chinese Journal of Mechanical Engineering 2018年第2期31卷 124-136页

作者： Jin Cheng Zhen-Yu Liu Jian-Rong Tan Yang-Yan Zhang Ming-Yang Tang Gui-Fang Duan State Key Laboratory of Fluid Power&Mechatronic Systems Zhejiang University State Key Laboratory of CAD&CG Zhejiang University Key Laboratory of Micro-systems and Micro-structures Manufacturing of Ministry of Education Harbin Institute of Technology

For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.

关键词： Robust optimization Uncertain structure Interval violation vector Feasibility robustness Objective robustness nested genetic algorithm

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A nested genetic algorithm FOR TOPOLOGYOPTIMIZATION IN LRPON

A NESTED GENETIC ALGORITHM FOR TOPOLOGYOPTIMIZATION IN LRPON

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2012 3rd IEEE International Conference on Network Infrastructure and Digital Content

作者： Xiaoxu Liu,Rentao Gu,Yuefeng Ji Beijing Key Laboratory of Network System Architecture and Convergence,School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China

This paper researches and formulates a topology optimization model in long-reach passive optical network(LRPON).The objective is to minimize the deployment cost with certain physical constraints.A nested genetic algorithm(NGA) is proposed to solve the topology optimization *** NGA consists of two loops,and the outer-loop GA(OLG) is a simple genetic algorithm(SGA) to solve the location problem of splitters,while the inner-loop GA(ILG) is a partheno-genetic algorithm(PGA) to complete the construction of the spanning ***,three scenarios with different network scales are simulated to examine the efficiency of the proposed algorithm.

关键词： nested genetic algorithm LRPON Topology optimization

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A nested genetic algorithm FOR TOPOLOGYOPTIMIZATION IN LRPON

A NESTED GENETIC ALGORITHM FOR TOPOLOGYOPTIMIZATION IN LRPON

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The 3rd IEEE International Conference on Network Infrastructure and Digital Content (2012年第三届IEEE网络基础设施与数字内容国际会议(IEEE IC-NIDC2012))

作者： Xiaoxu Liu Rentao Gu Yuefeng Ji Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication EngineeringBeijing University of Posts and Telecommunications Beijing 100876 China

This paper researches and formulates a topology optimization model in long-reach passive optical network (LRPON).The objective is to minimize the deployment cost with certain physical constraints.A nested genetic algorithm (NGA) is proposed to solve the topology optimization *** NGA consists of two loops,and the outer-loop GA (OLG) is a simple genetic algorithm (SGA) to solve the location problem of splitters,while the inner-loop GA (ILG) is a partheno-genetic algorithm (PGA) to complete the construction of the spanning ***,three scenarios with different network scales are simulated to examine the efficiency of the proposed algorithm.

关键词： nested genetic algorithm LRPON Topology optimization

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Planting date recommendation for chili and tomato based on economic value prediction of agricultural commodities

Open Agriculture Journal

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Open Agriculture Journal 2018年第1期12卷 156-163页

作者： Nhita, Fhira Saepudin, Deni Adiwijaya Wisesty, Untari Novia School of Computing Telkom University Jl. Telekomunikasi No.1 Bandung Indonesia

Introduction: Chili and tomato are a type of fruit vegetable plant that is very often consumed by Indonesians in their daily diet. Fluctuating commodity prices are causing anxiety among consumers, especially farmers. The price of these commodities, especially chilies, has become a major concern for the Indonesian government. Price changes occur due to several factors: product quality, market demand, and production costs. The quality of the product is determined by the growth process of the commodity. If the selling price is influenced by product quality and product quality is influenced by weather, then there is an indirect correlation between price and weather. Material and Method: In this study, the selling price of chilies and tomatoes was predicted using a modified Artificial Neural Network algorithm, optimized by a nested genetic algorithm. Results: The results were then classified to obtain economic price and planting recommendations. The price is said to be economic in value if the selling price is greater than the cost of production. Farmers can use planting recommendations to decide the right planting period, so that there is no harvest failure. In addition, using this information, farmers will not suffer from losses due to selling prices that cannot cover production costs. Conclusion: The results obtained from this experiment show that there is a match of the values of economic price and planting date recommendation for chilies and tomatoes, with an accuracy of 81% and 100%, respectively. © 2018 Nhita et al.

关键词： Artificial netural network Chili Economic value nested genetic algorithm Planting date Tomato

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The optimization of worker's quantity based on cross-utilization in many departments

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2017年第1期11卷 15-28页

作者： Liu, Huizhen Zhengzhou Univ Aeronaut Sch Management Sci & Engn Collaborat Innovat Ctr Aeronaut Econ Dev Henan Pr Zhengzhou Henan Peoples R China

Many manufacturing and service firms operate with a workforce that is partitioned into two or more work categories (skills) to satisfy demands and reduce worker costs. In this paper, we describe a multi-skill, cross-utilization problem in many departments, and a general framework which may help in trade-offs between worker cost and met demands is developed by a two-stage stochastic programming model. To reduce the cost for total workers, the unmet demands are permitted and will be penalized. For optimizing the number of workers staffed in many departments, factors such as the worker cost coefficient, the penalty factor and the worker efficiency are considered. In Stage 1, optimization model considered in this paper is to minimize the sum of the cost for total workers and penalty, and in Stage 2 the model seeks to minimize the penalty, which is quantified by the unmet demands during assigning multi-skilled workers across departments. We propose a nested genetic algorithm to solve the proposed two-stage stochastic programming model. Finally, numerical studies are performed to examine the efficiency of the proposed algorithm.

关键词： Cross-utilization worker cost worker assignment stochastic programming nested genetic algorithm

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A Stackelberg game theoretic model for optimizing product family architecting with supply chain consideration

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2016年 172卷 1-18页

作者： Wang, Danping Du, Gang Jiao, Roger J. Wu, Ray Yu, Jianping Yang, Dong Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Univ Westminster London W1R 8AL England Donghua Univ Sch Business & Management Shanghai 200051 Peoples R China

Planning of an optimal product family architecture (PFA) plays a critical role in defining an organization's product platforms for product variant configuration while leveraging commonality and variety. The focus of PFA planning has been traditionally limited to the product design stage, yet with limited consideration of the downstream supply chain-related issues. Decisions of supply chain configuration have a profound impact on not only the end cost of product family fulfillment, but also how to design the architecture of module configuration within a product family. It is imperative for product family architecting to be optimized in conjunction with supply chain configuration decisions. This paper formulates joint optimization of PFA planning and supply chain configuration as a Stackelberg game. A nonlinear, mixed integer bilevel programming model is developed to deal with the leader-follower game decisions between product family architecting and supply chain configuration. The PFA decision making is represented as an upper-level optimization problem for optimal selection of the base modules and compound modules. A lower-level optimization problem copes with supply chain decisions in accordance with the upper-level decisions of product variant configuration. Consistent with the bilevel optimization model, a nested genetic algorithm is developed to derive near optimal solutions for PFA and the corresponding supply chain network. A case study of joint PFA and supply chain decisions for power transformers is reported to demonstrate the feasibility and potential of the proposed Stackelberg game theoretic joint optimization of PFA and supply chain decisions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Product family architecting Supply chain configuration Stackelberg game Bilevel optimization nested genetic algorithm

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Direct optimization of uncertain structures based on degree of interval constraint violation

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COMPUTERS & STRUCTURES 2016年第Feb.期164卷 83-94页

作者： Cheng, Jin Liu, Zhenyu Wu, Zhenyu Tang, Mingyang Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China

A constrained interval optimization model is proposed for the optimization of uncertain structures with their mechanical performance indices described as the objective and constraint functions of the design vector and interval uncertain parameters. Present indirect approaches for solving such interval optimization models by converting them into deterministic ones will result in the loss of uncertainty information and deviate from the original intention of realistically modeling engineering optimization problems. To overcome these shortcomings, a novel optimization algorithm is proposed for directly solving the nonlinear constrained interval optimization models based on a novel concept of the degree of interval constraint violation (DICV) and the DICV-based preferential guidelines. A nested genetic algorithm (GA) is developed to realize the direct interval ranking of various design vectors. The outer layer GA locates the optimal solution based on direct interval ranking. The inner layer GM integrated with Kriging technique compute the intervals of the mechanical performance indices of every design vector in the current population of the outer layer GA. The validity and superiority of the proposed direct interval optimization algorithm was verified by three numerical examples. Finally, the proposed direct interval optimization method was applied to the optimization of the cone ring fixture with uncertain material properties in a large turbo generator aimed at moving its natural frequencies away from the exciting one. The results demonstrated its feasibility and effectiveness in optimizing practical engineering structures under uncertainties. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Uncertain structure Constrained interval optimization model Degree of interval constraint violation Direct interval ranking nested genetic algorithm Kriging model

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THE OPTIMIZATION DESIGN OF C2 ORGANIZATION COMMUNICATION NETWORK BASED ON nested genetic algorithm

THE OPTIMIZATION DESIGN OF C2 ORGANIZATION COMMUNICATION NET...

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International Conference on Machine Learning and Cybernetics

作者： Mu, Liang Xiu, Bao-Xin Huang, Jin-Cai Yao, Li Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Peoples R China

ISBN: (纸本)9781424447053

The Task Information Flow Oriented C2 Organization Communication Network Design Problem (TIFOCOCNDP) is investigated. The basic information flow in the C2 organization is analyzed, then the performance measures and the calculation model of C2 organization communication network are studied. A constrained optimization model for TIFOCOCNDP is built, and a solution method based on genetic algorithm is proposed. Afterwards, a nested genetic algorithm for solving this problem is designed, analysis and design work involving encoding, crossover, mutation, fitness function construction and algorithm nestification, etc. is carried out. In the final section of the paper, a case is presented to test the feasibility of the solution method. The validity of the genetic algorithm is analyzed through computational experiments.

关键词： Task information flow Organization communication network nested genetic algorithm

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THE OPTIMIZATION DESIGN OF C2 ORGANIZATION COMMUNICATION NETWORK BASED ON nested genetic algorithm

THE OPTIMIZATION DESIGN OF C2 ORGANIZATION COMMUNICATION NET...

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： LIANG MU BAO-XIN XIU JIN-CAI HUANG LI YAO School of Information Systems and Management National University of Defence Technology Changsha 410073 China

The Task Information Flow Oriented C2 Organization Communication Network Design Problem (TIFOCOCNDP) is investigated. The basic information flow in the C2 organization is analyzed, then the performance measures and the calculation model of C2 organization communication network are studied. A constrained optimization model for TIFOCOCNDP is built, and a solution method based on genetic algorithm is proposed. Afterwards, a nested genetic algorithm for solving this problem is designed, analysis and design work involving encoding, crossover, mutation, fitness function construction and algorithm nestification, etc. is carried out In the final section of the paper, a case is presented to test the feasibility of the solution method. The validity of the genetic algorithm is analyzed through computational experiments.

关键词： Task information flow Organization communication network nested genetic algorithm

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