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2020 Asia Energy and Electrical Engineering Symposium

作者： Li, Qichang Song, Qiang Wang, Lin Ni, Xinxin Shao, Shiwen Tan, Yuan Peng, Ying Si, Shaoqing State Grid Shandong Elect Power Co Jining Power Supply Co Jining 272000 Shandong Peoples R China Shandong Univ Sci & Technol Qingdao Coll Elect Engn & Automat Qingdao 266590 Shandong Peoples R China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167824

With the rapid development of intelligent distribution networks and demand-side response, the effective use of large-scale flexible loads is one of the main trends in the future distribution networks. The flexible load is divided into "source" load, "source load" load and "load" load, and a multi-objective programming model of distribution network frame based on flexible load is proposed. The model considers both the basic optimization of the planning problem and the operation control strategies of different flexible loads. The multi-objective discrete binary particle swarm algorithm is used to solve the model, and a constraint mechanism adapted to the characteristics of the distribution networks is proposed. A large number of infeasible solutions are removed, and iterative operations are performed to obtain the Pareto non-inferior solution set. Finally, the entropy weight the modified TOPSIS multi-attribute decision-making method is used to solve the optimal scheme as the final grid planning scheme. A 25-nodes distribution network planning system is used as an example to verify the validity and rationality of the proposed method.

关键词： distribution network frame planning flexible load multi-objective particle swarm optimization

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Effect of Capacity Tightness on Performance of MOPSO Algorithm: Case of multi-item Capacitated Lot-Sizing Problem 2nd

Effect of Capacity Tightness on Performance of MOPSO Algorit...

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2nd International Conference on Advanced Materials, Mechanics and Manufacturing, A3M 2018

作者： Ammar, Hanen Ben Ayadi, Omar Masmoudi, Faouzi Route de Sokra B.P Sfax1173-3038 Tunisia

ISBN: (纸本)9783030242466

This paper investigates the effect of capacity tightness on performance of multi-objective particle swarm optimization (MOPSO) algorithm in solving the multi-item capacitated lot-sizing problem with consideration of setup times and backlogging (MICLSP-SB). The considered problem is formulated as a multi-objective optimization model. The formulated model aims at simultaneously minimizing two objective functions. The first one seeks to minimize the total cost, which the sum of production, setup and backlogging costs. The second one seeks to minimize the total inventory level. Sensitivity analysis is performed on a set of generated problem instances. The capacity tightness factor is defined as the ratio between the required capacity and the total available capacity. Three levels of capacity tightness factor are considered for each problem instances. The metrics, which are used for evaluating the performances of the MOPSO algorithm, are number of Pareto solutions, spacing and computational time. Results of sensitivity analysis show a considerable impact of capacity tightness on performances of MOPSO algorithm in terms of number of Pareto solutions and computational time. This investigation offers to the decision makers a clear insight into the performances of MOPSO algorithm in solving the considered MICLSP-SB depending on problem features especially, the capacity tightness factor. © 2020, Springer Nature Switzerland AG.

关键词： Capacity tightness Lot-sizing multi-objective particle swarm optimization Production planning

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Fuzzy control optimized by a multi-objective particle swarm optimization algorithm for vibration suppression of smart structures

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STRUCTURAL AND multiDISCIPLINARY optimization 2011年第1期43卷 29-42页

作者： Marinaki, Magdalene Marinakis, Yannis Stavroulakis, Georgios E. Tech Univ Crete Dept Prod Engn & Management Khania 73100 Crete Greece Tech Univ Carolo Wilhelmina Braunschweig Inst Appl Mech Braunschweig Germany

Smart structures include elements of active, passive or hybrid control. In this paper, a new multi-objective particle swarm optimization (MOPSO), with a different velocity equation, for the calculation of the free parameters in active control systems is proposed and tested. A fuzzy control system is considered. Fuzzy control is a suitable tool for the systematic development of nonlinear active control strategies and can be fine tuned if no experience exists or if one designs more complicated control schemes. The usage of MOPSO with a combination of continuous and discrete variables for the optimal design of the controller is proposed. Numerical applications on smart piezoelastic beams are presented.

关键词： multi-objective particle swarm optimization Active control of structures Fuzzy control Smart structures

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Ground Motion Record Selection Using multi-objective optimization Algorithms: A Comparative Study

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2019年第3期63卷 812-822页

作者： Kaveh, Ali Moghanni, Roya Mahdipour Javadi, Seyed Mohammad Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Tehran *** Iran

Performing time history dynamic analysis using site-specific ground motion records according to the increasing interest in the performance-based earthquake engineering has encouraged studies related to site-specific Ground Motion Record (GMR) selection methods. This study addresses a ground motion record selection approach based on three different multi-objective optimization algorithms including multi-objective particle swarm optimization (MOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II). The method proposed in this paper selects records efficiently by matching dispersion and mean spectrum of the selected record set and target spectrums in a predefined period. Comparison between the results shows that NSGA II performs better than the other algorithms in the case of GMR selection.

关键词： ground motion record selection multi-objective particle swarm optimization non-dominated sorting genetic algorithm II pareto envelope-based selection algorithm

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Improvement of Time and Frequency Domain Performance of Antipodal Vivaldi Antenna Using multi-objective particle swarm optimization

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2011年第5期59卷 1738-1742页

作者： Chamaani, Somayyeh Mirtaheri, S. Abdullah Abrishamian, Mohammad S. KN Toosi Univ Technol Tehran *** Iran

The design of an antipodal Vivaldi antenna for ultra wideband (UWB) applications is presented. The main purpose of this design is the reduction of three parameters of transient distortion, reflection coefficient and cross polarization level. multi-objective particle swarm optimization (MOPSO) is applied to handle these objectives simultaneously. All antenna simulations are performed using the CST MWS software. Several design cases of the resultant Pareto fronts are compared, and finally, a sample of the Pareto front is fabricated. To assess the time domain performance of the antenna, two identical antennas are placed in an end-to-end orientation. The simulated correlation factor in this scenario is more than 93%. Measurement results show that vertical bar S-11 vertical bar of the developed antenna is less than -10 dB, the cross polarization level is below -11.5 dB and the gain is between 4.5 dBi and 9 dBi over the whole UWB frequency band.

关键词： Antipodal Vivaldi antenna correlation factor multi-objective particle swarm optimization ultrawideband antenna

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Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

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APPLIED SOFT COMPUTING 2011年第1期11卷 1427-1438页

作者： Qasem, Sultan Noman Shamsuddin, Siti Mariyam Univ Teknol Malaysia Fac Comp Sci & Informat Syst Soft Comp Res Grp Skudai 81310 Johor Malaysia

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization(TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Radial basis function network Hybrid learning particle swarm optimization Time variant multi-objective particle swarm optimization multi-objective particle swarm optimization Elitist non-dominated sorting genetic algorithm

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multi-objective optimization Design of Constant Stress Accelerated Degradation Test Using Inverse Gaussian Process

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IEEE ACCESS 2019年 7卷 28757-28768页

作者： Wu, Zhenyu Liu, Xiaoping Guo, Bin Cui, Dejun Zhang, Lijie Yanshan Univ Minist Educ China Key Lab Adv Forging & Stamping Technol & Sci Qinhuangdao 066004 Peoples R China Yanshan Univ Hebei Key Lab Heavy Machinery Fluid Power Transmi Qinhuangdao 066004 Peoples R China

A multi-objective optimization method for constant stress accelerated degradation test is proposed in order to solve the problem of different or even conflicted test configuration under different optimization objectives. The inverse Gaussian process is used as a degradation model, and the unknown parameters are solved by maximum likelihood estimation. The two optimization criteria of the maximum determinant of the information matrix and the minimum asymptotic variance of P-quantile are considered. The improved multi-objective particle swarm optimization algorithm is proposed to search test optimal configuration, and the Pareto solution set for dual-objectives is obtained. Finally, the effectiveness of the method is illustrated by a group of examples of electrical connectors. Compared with the single-objective optimization design, the proposed method is more reasonable and convenient for test configuration. The performance index of the test function indicates that the optimal algorithm we proposed has some obvious advantages over the NSGA-II in diversity and convergence of the Pareto solutions and it is significant in guiding engineering practice.

关键词： Accelerated degradation test optimal design multi-objective maximum likelihood estimation multi-objective particle swarm optimization

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Resource-Constrained Time-Cost Tradeoff for Repetitive Construction Projects

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KSCE JOURNAL OF CIVIL ENGINEERING 2019年第8期23卷 3265-3274页

作者： Heravi, Gholamreza Moridi, Saeed Univ Tehran Sch Civil Engn Coll Engn Tehran 111554563 Iran

The time-cost tradeoff problem is to find optimal combinations of construction options with the objective of minimizing project time and cost. In order to search for such a set of optimal solutions, the total time and cost of projects needs to be calculated properly. In repetitive construction projects (RCPs), due to division of work into several units and involvement of many resources for activities completion, scheduling is unique and more complicated. Scheduling method in this paper besides addressing common constraints such as precedence relationships, required lead time and lag distance between activities, enables project managers to consider all resources and their shortages in scheduling. To raise the practicality of model, in addition to direct and indirect costs, resource idle cost is considered as a cost element in estimating total cost of project. The time variant multi-objective particle swarm optimization is applied to find non-dominated solutions on the basis of minimizing time and cost of project. An application example is presented at the end to illustrate the performance of the model. This research presents a resource-constrained time-cost tradeoff model to find the optimal set of crew combinations for project activities in RCPs, considering time and cost simultaneously.

关键词： repetitive construction projects time-cost tradeoff multi-objective particle swarm optimization resource constraint line of balance optimization resource allocation

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MOPSO-based Data Scheduling Scheme for P2P Streaming Systems

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2019年第10期13卷 5013-5034页

作者： Liu, Pingshan Fan, Yaqing Xiong, Xiaoyi Wen, Yimin Lu, Dianjie Guilin Univ Elect Technol Business Sch Guilin Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Shangdong Peoples R China

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.

关键词： Peer-to-Peer video streaming data scheduling multi-objective optimization multi-objective particle swarm optimization

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Mathematical programming and three metaheuristic algorithms for a bi-objective supply chain scheduling problem

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NEURAL COMPUTING & APPLICATIONS 2019年第12期31卷 9073-9093页

作者： Zarei, Hamid Rasti-Barzoki, Morteza Isfahan Univ Technol Dept Ind & Syst Engn Esfahan *** Iran

In this study, a bi-objective optimization problem for a supply chain with different transportation modes is addressed. The first objective function is minimizing costs imposed by production, batching, due date assignment and transportation. The second one is minimizing inventory and tardiness costs. Also, a heuristic rule is developed to choose non-dominated transportation modes. Three metaheuristic algorithms including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm (NSGA-II) and hybrid NSGA-II (HNSGA-II) are customized to solve the problem. In addition, a theoretical improvement in the non-dominated sorting procedure called improved efficient non-dominated sorting (IENS) is proposed. Computational tests are used for comparing and evaluating the proposed methods and algorithms. The results show that IENS reduces running time compared to the modern method of non-dominated sorting, the efficient non-dominated sorting method, and this reduction is statistically significant. Also, the HNSGA-II has an average but robust performance compared to the other two algorithms.

关键词： Supply chain scheduling Non-dominated sorting genetic algorithm multi-objective particle swarm optimization Transportation modes

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