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RESOURCE ALLOCATION optimization STRATEGY USING IMPROVED DIFFERENTIAL EVOLUTION ALGORITHM IN multi-RADIO multi-CHANNEL WSNS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2020年第3期16卷 923-937页

作者： Qiu, Bin Shantou Polytech Dept Comp Haojiang Dist 515078 Shantou Peoples R China

In order to improve the performance of multi-radio and multi-channel wireless sensor networks (WSNs), a resource allocation optimization strategy using improved differential evolution algorithm in multi-radio and multi-channel WSNs is proposed. Firstly, considering the interaction among WSNs node energy consumption, channel allocation, power control and slot allocation, the channel model of multi-node communication and the interference model of the system are constructed. Then, taking the interference and conflict of links as constraints, and taking the objects that reduce energy consumption, increase network capacity and improve the balance of resource allocation as objective functions, a multi-objective optimization model of resource allocation is constructed. The establishment of multi-objective optimization model highlights the trade-off among different objectives. Two-population differential evolution algorithm is employed to solve the model constructed in this paper, and the results are compared with those of standard differential evolution algorithms. The experimental results show that the performance of the network in all aspects has been greatly improved by the use of the proposed algorithm. The simulation results show that the link collisions can be effectively avoided and the network interference is reduced with the utilization of the proposed algorithm. Compared with the existing algorithms, the capacity of key links can be raised by two to three times, and the network capacity and resource allocation balance are improved as a result of application of the proposed algorithm.

关键词： Resource allocation optimization Wireless sensor networks multi-radio multi-channel Improved differential evolution algorithms multi-objective optimization model Slot allocation Link capacity

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A multi-SATELLITE REGIONAL IMAGING MISSION PLANNING METHOD BASED ON MOOM FOR EMERGENCY SURVEYING AND MAPPING 39

A MULTI-SATELLITE REGIONAL IMAGING MISSION PLANNING METHOD B...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chen, Yaxin Shen, Xin Li, Shixue Zhang, Guo Xu, Miaozhong Liu, Yulin Xu, Junfei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Geospatial Informat Technol Collaborat Innovat Ct Wuhan 430079 Peoples R China

ISBN: (纸本)9781538691540

Aiming at the imaging problem of regional target in emergency surveying and mapping, a multi-objective optimization model(MOOM) is proposed, which takes the imaging lateral swing angles of satellite as decision variables and takes the maximum coverage rate of regional target and the minimum number of holes as objective functions. Aiming at the two key problems of evaluation function calculation and multi-objective model solving, Vatti algorithm and NSGAII algorithm are used to solve them respectively. Finally, STK simulation data are used to verify the feasibility of the optimization method.

关键词： Agile satellite Emergency surveying and mapping Regional target imaging multi-objective optimization model

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multi-objective Operation Mode optimization of Medium Voltage Distribution Networks based on Improved Binary Particle Swarm optimization 4

Multi-objective Operation Mode Optimization of Medium Voltag...

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4th International Conference on Intelligent Green Building and Smart Grid (IGBSG)

作者： He, Cai Li, Dan China Three Gorges Univ Coll Elect Engn & New Energy Yichang Hubei Peoples R China Hubei Prov Collaborat Innovat Ctr New Energy Micr Yichang Hubei Peoples R China

ISBN: (纸本)9781728121482

Considering the operation economy and power quality of power distribution networks, a multi-objective optimization model of medium voltage intelligent distribution networks with objective functions of minimum network loss and switching frequency and maximum voltage deviation has established. The model has solved by an improved binary particle swarm optimization (BPSO) algorithm introducing nonlinear dynamic adjustment of learning factor and inertial weight coefficient. The results of numerical calculation show that the proposed model can balance the network loss, voltage quality and frequency of switch actions compared with the traditional single-objective optimization model, and the proposed algorithm can not only balance global search ability and local search ability of the PSO algorithm, but also make the algorithm own faster convergence. Feasibility and validity of the proposed model and algorithm have been verified from the application.

关键词： Distribution network multi-objective optimization model Improved BPSO Nonlinear dynamic adjustment factor

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COST AND SERVICE-LEVEL-BASED model FOR A SERU PRODUCTION SYSTEM FORMATION PROBLEM WITH UNCERTAIN DEMAND

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系统科学与系统工程学报（英文版） 2018年第4期27卷 519-537页

作者： Ye Wang Jiafu Tang School of Management Science and Engineering Dongbei University of Finance and Economics(DUFE)Dalian 116025 China

With increasing demand diversification and short product lifecycles,industries now encounter challenges of demand *** Japanese seru production system has received increased attention owing to its high efficiency and *** this paper,the problem of seru production system formation under uncertain demand is researched.A multi-objective optimization model for a seru production system formation problem is developed to minimize the cost and maximize the service level of the *** purpose of this paper is to formulate a robust production system that can respond efficiently to the stochastic *** average approximation (SAA) is used to approximate the expected objective of the stochastic *** non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) is improved to solve the multi-objective optimization *** experiments are conducted to test the tradeoffbetween cost and service level,and how the performance of the seru production system varies with the number of product types,mean and deviation of product volume,and skill-level-based cost.

关键词： Japanese cellular manufacturing seru production system service level multi-objective optimization model

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Study on an airport gate assignment method based on improved ACO algorithm

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KYBERNETES 2018年第1期47卷 20-43页

作者： Deng, Wu Sun, Meng Zhao, Huimin Li, Bo Wang, Chunxiao Dalian Jiaotong Univ Software Inst Dalian Peoples R China Sichuan Univ Sci & Engn Sichuan Prov Key Lab Proc Equipment & Control Zigong Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning Peoples R China Dalian Jiaotong Univ Liaoning Key Lab Welding & Reliabil Rail Transpor Dalian Peoples R China Dalian Jiaotong Univ Dalian Key Lab Welded Struct & Its Intelligent Mf Dalian Peoples R China

Purpose - This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement. Design/methodology/approach - In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period. Findings - In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed. Practical implications - The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a verymeaningful work for airport gate assignment. Originality/value - An efficient multi-objective optimization

关键词： Robust optimization Performance analysis Airport gate assignment Improved ant colony optimization algorithm multi-objective optimization model

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Incentive-aware virtual machine scheduling in cloud computing

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JOURNAL OF SUPERCOMPUTING 2018年第7期74卷 3016-3038页

作者： Xu, Heyang Liu, Yang Wei, Wei Zhang, Wenqiang Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China

As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multi-objective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful execution rate, lower execution cost, smaller fairness deviation and most important, higher degree of user satisfaction in most cases.

关键词： Cloud computing Virtual machine scheduling Incentives multi-objective optimization model User satisfaction

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An optimization model for water quantity and quality integrated management of an urban lake in a water deficient city

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URBAN WATER JOURNAL 2017年第9期14卷 922-929页

作者： Zheng, Jian-Feng Jiao, Ji-Dong Zhang, Shuo Sun, Li-Ping Tianjin Chengjian Univ Sch Environm & Municipal Engn Tianjin Key Lab Aquat Sci & Technol Tianjin Peoples R China

This paper presents an optimization model for water quantity and quality integrated management of an urban lake in a water deficient city. A representative water quantity and quality safeguard system served urban lake, including multi-source water supply facilities, recirculating water purification facilities and surplus water discharge facilities, is widely used in Chinese water deficient cities. Because it is complicated, any mismanagement will result in water quality deterioration, water waste and high operation cost. The presented model attempts to achieve the objectives of controlling water pollution, reducing economic cost and improving water utilization efficiency through an optimized operating water safeguard system. The model is applied to Qingjing Lake in Tianjin, China. Results show that the model plays a more positive role for water quantity and quality integrated management.

关键词： Urban lake water deficient city water quantity and quality safeguard system multi-objective optimization model

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A data-driven air cargo redistribution model based on multiple programming

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2019年第17期33卷

作者： Peng, Yang Wang, Pu Zhao, Xiaolong Chen, Meilin Zhang, Jun Zhang, Fan Cent S Univ Sch Traff & Transportat Engn Changsha 410000 Hunan Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China

Air cargo transportation is an essential mode of cargo transportation. How to distribute air cargo into flights better is an important operational problem. In this paper, air cargo data collected by CAAC (Civil Aviation Administration of China) during the first three months of 2018 are analyzed. We find that the available capacity for cargo transportation shows great variations, and the cargo compartment utilization rates of flights are heterogeneously distributed. Next, a data-driven air cargo redistribution model is developed based on multiple programming (MP). The proposed model can effectively transport high-priority goods and balance cargo compartment utilization rates of flights. In addition, the proposed model framework can provide a new solution to multi-objective or multi-stage optimization problems.

关键词： Data mining and data analysis air cargo transportation multi-objective optimization model

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Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment

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APPLIED SOFT COMPUTING 2017年 59卷 288-302页

作者： Deng, Wu Zhao, Huimin Yang, Xinhua Xiong, Juxia Sun, Meng Li, Bo Dalian Jiaotong Univ Software Inst Dalian 116028 Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Sichuan Univ Sci & Engn Sichuan Prov Key Lab Proc Equipment & Control Zigong 64300 Peoples R China Dalian Jiatong Univ Dalian Key Lab Welded Struct & Its Intelligent Mf Rail Transportat Equipment Dalian 116028 Peoples R China Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the effective connection between flights and improve the capacity and service efficiency of airport. The minimum walking distances of passengers, the minimum idle time variance of each gate, the minimum number of flights at parking apron and the most reasonable utilization of large gates are selected as the optimization objectives, then an efficient multi-objective optimization model of gate assignment problem is proposed in this paper. Then an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus is deeply studied. The dynamic fractional calculus with memory characteristic is used to reflect the trajectory information of particle updating in order to improve the convergence speed. The Alpha-stable distribution theory is used to replace the uniform distribution in order to escape from the local minima in a certain probability and improve the global search ability. Next, the DOADAPO algorithm is used to solve the constructed multi-objective optimization model of gate assignment in order to fast and effectively assign the gates to different flights in different time. Finally, the actual flight data in one domestic airport is used to verify the effectiveness of the proposed method. The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improve the comprehensive service of gate assignment. It can effectively provide a valuable reference for assigning the gates in hub airport. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Gate assignment Adaptive particle swarm optimization multi-objective optimization model Dynamic fractional calculus Alpha-stable distribution theory

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A cost-emission framework for hub energy system under demand response program

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ENERGY 2017年 134卷 157-166页

作者： Majidi, Majid Nojavan, Sayyad Zare, Kazem Univ Tabriz Fac Elect & Comp Engn POB 51666-15813 Tabriz Iran

Based on the kind of fuel consumed by generation unit, each generation system has different generation costs and emits various types of greenhouse gases like CO2, SO2 and NO2 to the atmosphere. So, nowadays in the power system scheduling, emission issue has been turned to be an important factor. In this paper, in addition to economic performance, emission problem of energy hub system has been also investigated. Therefore, a multi-objective optimization model has been proposed for cost-environmental operation of energy hub system in the presence of demand response program (DRP). Weighted sum approach has been employed to solve the proposed multi-objective model and fuzzy satisfying technique has been implemented to select the best compromise solution. Implementation of load management programs presented by DRP shifts some percentage of load from peak periods to off-peak periods to flatten load curve which leads to reduction of total cost and emission of energy hub system. A mixed integer linear programming has been used to model the cost-environmental performance problem of energy hub system and then, GAMS optimization software has been utilized to solve it. A sample energy hub system has been studied and the obtained results have been compared to validate the effectiveness of proposed techniques. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization model multi-carrier energy system Combined heat and power Weighted sum approach Fuzzy satisfying techniques Demand response program

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