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11th IEEE International Conference on Software Engineering and Service Science (IEEE ICSESS)

作者： Jin, Jian Wang, Aiping Univ Anhui Dept Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165790

Successive Interference Cancellation(SIC) is a multiuser detection algorithm, which can effectively combat multiple access interference in wireless networks. In wireless network system, the network throughput can be improved by using serial interference elimination algorithm and the transmitting power of control nodes. However, in practical applications, pursuing network throughput should also consider saving system energy consumption. In response to this situation, a multiple-objective power control model based on a successive interference cancellation is established with two optimization objectives, minimize the total system transmit power and maximize the average network throughput of the system nodes. An improved NSGA-II algorithm is used to optimize the model globally. A clustering distance-based elimination strategy is designed to maintain the population distribution. The effects of the improved NSGA-II algorithm on the problem are analyzed through simulation experiments. The results of 20 groups of contrastive experiments show that the Pareto solution obtained by the proposed algorithm is superior to the PESA-II algorithm and the MOPSO algorithm.

关键词： Wireless Network Power Control Successive Interference Cancellation multiple-objective optimization

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Parametrically computing efficient frontiers and reanalyzing efficiency-diversification discrepancies and naive diversification

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INFOR 2019年第3期57卷 430-453页

作者： Qi, Yue Zhang, Yushu Ma, Siyuan Nankai Univ China Acad Corp Governance 94 Weijin Rd Tianjin 300071 Peoples R China Nankai Univ Dept Financial Management Business Sch 94 Weijin Rd Tianjin 300071 Peoples R China Nankai Univ Collaborat Innovat Ctr China Econ 94 Weijin Rd Tianjin 300071 Peoples R China Stevens Inst Technol Sch Business 1 Castle Point Terrace Hoboken NJ USA

Portfolio selection is recognized as the birth-place of modern finance. Weighted-sums methods or e-constraint methods are normally utilized for portfolio optimization, but the results are only approximations of efficient frontiers. One concern of portfolio selection is efficiency-diversification discrepancies that efficient frontiers lack diversification. Scholars typically analyze the discrepancies by using weighted-sums methods or e-constraint methods, studying only a specific portfolio, and utilizing small-scale portfolio selection. Some scholars find that portfolio selection is not consistently better than naive diversification. We utilize parametric quadratic programming, exhaustively sample US stocks, build batches of 5-stock problems up to 1800-stock problems, obtain the structure of (whole) efficient frontiers, and propose new diversification measures on the basis of the structure. We find that (1) setting upper bounds can be more effective in changing diversification status than setting right-hand sides or setting the numbers of constraints can, (2) portfolio selection can substantially outperform naive diversification at least in sample so the cost of naive diversification can be prohibitive, and (3) efficiency-diversification discrepancies can arise due to efficient frontiers' nature of having relatively small numbers of stocks and cannot be easily reconciled.

关键词： multiple-objective optimization portfolio optimization parametric quadratic programming efficient set diversification

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Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics

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COMPUTERS & OPERATIONS RESEARCH 2019年 110卷 220-235页

作者： Govindan, Kannan Jafarian, Ahmad Nourbakhsh, Vahid Univ Southern Denmark Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Odense Denmark ATU Fac Management & Accounting Tehran Iran Univ Calif Irvine Paul Merage Sch Business Irvine CA 92697 USA

Recently, a growing concern with sustainability has become a consideration in business operations. However, there is a lack of mathematical models that quantify environmental effects and, in particular, social impacts of supply chains because of the inherently subjective nature of these aspects. To fill this gap, this paper models a distribution network in which the triple bottom lines of sustainability are captured. Different impacts of the network on the stakeholders, including company owners, workers, consumers and society, are considered as whole. In the current model, a multi-product vehicle routing problem with time windows (MPVRPTW) as an operational decision is integrated with strategic decisions related to the network design. To solve this model, three hybrid swarm intelligence techniques (particle swarm optimization (PSO), electromagnetism mechanism algorithm (EMA), and artificial bee colony (ABC)) are proposed, and each is hybridized with variable neighborhood search (VNS) are proposed. Because metaheuristic methods are sensitive to input parameters, response surface methodology (RSM) with the multi-objective decision making (MODM) approach is applied for tuning the parameters. The proposed approaches are compared with the hybrid of genetic algorithm (GA) and VNS as the benchmark algorithm. A fair comparison is conducted by employing six metrics to evaluate the quality of the Pareto frontier obtained by the algorithms on the test problems. According to the results, the predominance of EMA is enhanced by VNS local search. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sustainable supply chain network Environmental management Corporate social responsibility multiple-objective optimization Vehicle routing problem Swarm intelligence algorithms

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Localization of Interference Sources in LTE Mobile Networks 4

Localization of Interference Sources in LTE Mobile Networks

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4th International Conference on Communication and Information Systems (ICCIS)

作者： Jiang, Tingjuan Ye, Wen Yin, Jingping Xiong, Shengxiang Jin, Haijia Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781728162973

Interference seriously affects wireless signal transmission and degrades the quality of services in LTE mobile communication networks. In a dense urban environment, extra-network interference signals undergo multipath and non-light-of-sight propagation, and it is urgent but difficult for network operators to localize the interference sources quickly and accurately. This paper proposes a method of using propagation path analysis to localize extra-network interference sources. The method selects the measurement points with strong received signal strengths from frequency-sweeping measurement reports, employs reverse ray tracing to generate possible line-of-sight, non-line-of-sight, and multipath signal propagation trajectories between the candidate inference sources and the measurement points, and then takes multi-objective optimization to position the interference sources. We use practical LTE network data to conduct simulations to evaluate the proposed localization scheme. The results show that in a dense urban environment, the path-analysis-based interference localization can achieve positioning accuracy with deviation within 50 meters and therefore provides an efficient means for interference troubleshooting in LTE networks.

关键词： LTE network interference localization ray tracing propagation path multiple-objective optimization

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Task Value Calculus: Multi-objective Trade off Analysis using multiple-Valued Decision Diagrams 49

Task Value Calculus: Multi-objective Trade off Analysis usin...

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49th IEEE International Symposium on multiple-Valued Logic (ISMVL)

作者： Giallanza, Tyler Gabrielsen, Erik Taylor, Michael A. Larson, Eric C. Thornton, Mitchell A. Southern Methodist Univ Darwin Deason Inst Cybersecur Dallas TX 75275 USA

ISBN: (纸本)9781728100920

Most multiple-objective optimization algorithms utilize continuous input variables. Given that many decision variables in common use-cases are discrete rather than continuous, we develop a multiple-objective optimization framework over discrete variables known as task value calculus (TVC). The underlying mathematical models in TVC utilize a multiple valued algebraic framework where both the objective functions and the system or process structure models are represented as multiple-valued functions. TVC allows for fast multiple-objective optimization through the use of the multiple-valued decision diagram (MDD) data structure. The algorithms and structures internal to TVC are described and experimental results are provided. TVC is implemented with a simple graphical user interface making it suitable for use by both laypersons and domain experts.

关键词： multiple-valued logic multiple-objective optimization multiple-valued decision diagram

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On outperforming social-screening-indexing by multiple-objective portfolio selection

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期267卷 493-513页

作者： Qi, Yue Nankai Univ China Acad Corp Governance 94 Weijin Rd Tianjin 300071 Peoples R China Nankai Univ Dept Financial Management Business Sch 94 Weijin Rd Tianjin 300071 Peoples R China Nankai Univ Collaborat Innovat Ctr China Econ 94 Weijin Rd Tianjin 300071 Peoples R China

Socially responsible investment has been rapidly growing over the past two decades and is typically fulfilled by screening and indexing. Recently, scholars propose multiple-objective portfolio selection for corporate social responsibility (CSR). The proposal raises the question whether multiple-objective portfolio selection can outperform screening and indexing. The question is not fully answered although researchers have made some encouraging trial. By formulating multiple-objective portfolio selection for CSR, I propose a theorem to demonstrate that investors can outperform screening and indexing in expected CSR with identical or better expected return and with identical variance, and can outperform screening and indexing in expected return with identical or better expected CSR and with identical variance. I empirically test the outperformance by component stocks of Dow Jones Industrial Average and report the results.

关键词： multiple-objective optimization multiple-objective portfolio selection Nondominated set Paraboloid Socially responsible investment (SRI)

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A Multi-objective optimization Approach for Question Routing in Community Question Answering Services

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2017年第9期29卷 1779-1792页

作者： Cheng, Xiang Zhu, Shuguang Su, Sen Chen, Gang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Community Question Answering (CQA) has increasingly become an important service for people asking questions and providing answers online, which enables people to help each other by sharing knowledge. Recently, with accumulation of users and contents, much concern has arisen over the efficiency and answer quality of CQA services. To address this problem, question routing has been proposed which aims at routing new questions to suitable answerers, who have both high possibility and high ability to answer the questions. In this paper, we formulate question routing as a multi-objective ranking problem, and present a multi-objective learning-to-rank approach for question routing (MLQR), which can simultaneously optimize the answering possibility and answer quality of routed users. In MLQR, realizing that questions are relatively short and usually attached with tags, we first propose a tagword topic model (TTM) to derive topical representations of questions. Based on TTM, we then develop features for each question-user pair, which are captured at both platform level and thread level. In particular, the platform-level features summarize the information of a user from his/her history posts in the CQA platform, while the thread-level features model the pairwise competitions of a user with others in his/her answered threads. Finally, we extend a state-of-the-art learning-to-rank algorithm for training a multi-objective ranking model. Extensive experimental results on real-world datasets show that our MLQR can outperform state-of-the-art methods in terms of both answering possibility and answer quality.

关键词： Community question answering services question routing multiple-objective optimization

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Novel strategy using crash tubes adaptor for damage levels manipulation and total weight reduction

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THIN-WALLED STRUCTURES 2017年第Feb.期111卷 176-188页

作者： Esa, Moustafa Xue, Pu Zahran, Mohamed Abdelwahab, Mostafa Khalil, Mohamed Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Mil Tech Coll Dept Civil Engn Cairo Egypt Mil Tech Coll Aircraft Mech Dept Cairo Egypt

Absorbing energy by crash tubes has been extensively employed in many applications to improve the crashworthiness of the structure. This paper proposes Piecemeal Energy Absorption (PEA) as a novel proposed strategy to perform crashworthiness in a gradual way. PEA is implemented by crash tubes adaptor, whilst this adaptor is composed of nested pieces. Based on geometrical dimensions of the nested tubes, PEA strategy generates lower damage under lower impact velocity and higher energy absorption under high impact velocity to improves the crashworthiness of the protected structure. The novel strategy is validated by numerical and experimental methods. Furthermore, PEA flexibility and capability are illustrated by implementing the strategy in two popular applications (subway buffer and UAV landing gear). Due to the generality and applicability of the proposed PEA strategy, it is recommended to replace existing vehicles absorbers in order to manipulate the damage levels, along with considering PEA roles in new crashworthy designs leads to redistribute and reduce the whole internal protected structure masses.

关键词： Crushing Crash tube multiple-objective optimization Axial impact Energy absorption Gradual landing gear Railway buffer Strategy

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Multi-objective optimization based on Pareto Optimum in Secondary Cooling and EMS Of Continuous Casting

Multi-objective Optimization based on Pareto Optimum in Seco...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Yang, Jie Ji, Zhenping Liu, Shuai Jia, Qiong Shenyang Ligong Univ Inst Automat & Elect Engn Shenyang Peoples R China

ISBN: (纸本)9781509033645

The process of Continuous casting cooling is a nonlinear, strong coupling, multi-input multi-output (MIMO) system, in existing control system of continuous casting of secondary cooling control and the final electromagnetic stirring(F-EMS) control are two independent control system. The optimization of control parameter often use two independent model, without considering the coupling effect between them, led to the slab quality fluctuation with changes on process parameter or steel. In order to solve the parameter optimization problem of the secondary cooling and electromagnetic stirring, the particle swarm with Pareto frontier multi-objective coordinated optimization algorithm is employed, combined with the most concerned requirements in temperature field, liquid core, which includes the temperature of raising and dropping in each cooling zone, and the liquid core radius satisfied the best electromagnetic stirring condition as the objective functions. Adopt adaptive grid system to evaluate the diversity of population, and to improve the consistency of the optimal solution set, using multidimensional vector weighting to process weights parameters of candidate solution set to obtain the final Pareto optimal solutions, finally, applied the optimization algorithm to continuous casting process, and verify the effectiveness and feasibility.

关键词： Pareto Frontier multiple-objective optimization Adaptive Grid Particle Swarm optimization Continuous Casting

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Design of reconfigurable flow lines using MOPSO and maximum deviation theory

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第5-8期84卷 1587-1600页

作者： Goyal, Kapil Kumar Jain, Pramod Kumar MM Univ Mullana Ambala India IIT Roorkee Mech & Ind Engn Dept Roorkee 247667 Uttar Pradesh India

Reconfigurable manufacturing system (RMS) is a state-of-the-art technology, which offers the exact capacity and functionality needed, through rapid and cost-effective reconfigurations in its modular structure. The reconfigurable machine tools (RMTs) can perform a variety of operations in its existing configuration and can further be reconfigured to change the operational capabilities. Thus, the selection of a machine configuration plays a vital role not only in the cost-effectiveness but in the future reconfiguration effort requirements. In the present study, authors have attempted a multiple-objective optimization of RMS flow line configuration, taking the cost, machine utilization, operational capability, and configuration convertibility as the conflicting objectives. The cost and machine utilization represent the economic justification while the operational capability and the configuration convertibility portray the ability of rapid and cost-effective reconfiguration ability. The emphasis of this research is to design the economic RMS configuration, which can rapidly and cost-effectively respond to the volatile market conditions. multiple-objective particle swarm optimization (MOPSO) has been implemented to handle the discrete and discontinuous search space. The large number of Pareto frontiers is further ranked to avoid subjectiveness and imprecision in the decision making, based on the composite scores obtained through maximum deviation theory. A numerical illustration is presented to illustrate the developed methodology of reconfigurable flow line configuration design.

关键词： Reconfigurable manufacturing systems (RMSs) Reconfigurable machine tool (RMT) Machine selection multiple-objective optimization multiple-objective particle swarmoptimization (MOPSO) Maximum deviation method

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