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2016 IEEE Information Technology,Networking,Electronic and Automation Control Conference(ITNEC 2016)

作者： Mingfeng Ying Xiaohui Mo Jin Jiang Jinling Institute of Technology

ISBN: (纸本)9781467391955

Based on the kinematics of an industrial robot, the three indices, including global kinematic average value, global kinematic volatility value, global kinematic worst value are analyzed and optimized by multi-objective optimization algorithm. The link length parameters of robot are design as the variable, and the multi-objective optimization model is established by the requirements of the robot workspace and kinematics. An improved multi-objective particle swarm optimization(pso) algorithm is proposed by using the distribution entropy and its difference of an approximate Pareto front to assess the diversity and evolutionary status of the population, and getting feedback information to design evolution strategy. Finally, a group of optimum link length is calculated by using this method. The optimized link length parameters of robot can improve the performance index of the robot greatly.

关键词： robot kinematics performance index Pareto entropy multi-objective particle swarm optimization

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MODELING ENGINE FUEL CONSUMPTION AND NOx WITH RBF NEURAL NETWORK AND MOPSO ALGORITHM

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2015年第6期16卷 1041-1049页

作者： Martinez-Morales, J. D. Palacios-Hernandez, E. R. Velazquez-Carrillo, G. A. Autonomous Univ San Luis Potosi Multidisciplinary Acad Unit Rioverde 79617 Mexico Autonomous Univ San Luis Potosi Fac Sci San Luis Potosi 78290 Mexico Monterrey Inst Technol & Higher Educ Dept Mech Mexico City 01389 DF Mexico

In this study, artificial neural network (ANN) modeling is used to predict the fuel consumption and NOx emission of a four stroke spark ignition (SI) engine. Calibration engineers frequently want to know the responses of an engine for the entire range of operating conditions in order to change engine control parameters in the electronic control unit (ECU), to improve performance and reduce emissions. However, testing the engine for the complete range of operating conditions is a very time and labor consuming task. As alternative, ANN is used in order to predict fuel consumption and NOx emission. In the proposed approach, the multi-objective particle swarm optimization (MOPSO) is used to determine weights of radial basis function (RBF) neural networks. The goal is to minimize performance criteria as root mean square error (RMSE) and model complexity. A sensitivity analysis is performed on MOPSO parameters in order to provide better solutions along the optimal Pareto front. In order to select a compromised solution among the obtained Pareto solutions, a fuzzy decision maker is employed. The correlation coefficient R-2 is used to compare the engine responses with the obtained by the proposed approach.

关键词： multi-objective particle swarm optimization Artificial neural network Spark ignition engine Exhaust emissions

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Inclusion of Blackouts Risk in Probabilistic Transmission Expansion Planning by a multi-objective Framework

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IEEE TRANSACTIONS ON POWER SYSTEMS 2015年第5期30卷 2810-2817页

作者： Karimi, Ebrahim Ebrahimi, Akbar IUT Dept Elect & Comp Engn Esfahan Iran

Risk of blackouts is more than expected and must be considered in the power system studies. The interest of this paper is to minimize the blackouts risk during minimizing the costs in probabilistic transmission expansion planning (TEP). In this paper, a long-term reliability analysis, which was developed by self-organized criticality theory, is used in a multi-objective optimization to include risk of blackouts in TEP. The objective functions are costs and expected energy not supplied. Also, a new formulation for calculating transmission operating cost as a part of cost function is proposed. The problem is solved by a multi-objective particle swarm optimization to find an optimal Pareto set from which the decision-maker can choose the most desirable one, regarding existing budget. The proposed method is applied to the IEEE 118-bus test system to investigate its applicability and scalability. The results are compared with security-constrained TEP, which uses famous deterministic criterion known as N-1. The proposed approach produces more efficient plans for suppressing cascading blackouts with less network investment. Moreover, the results provide motivation for considering the risk of cascading blackouts in TEP, which afford substantial saving to society.

关键词： Blackout risk cascading outages long-term reliability assessment multi-objective particle swarm optimization power law transmission expansion planning

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multi-objective portfolio optimization considering the dependence structure of asset returns

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第2期244卷 525-539页

作者： Babaei, Sadra Sepehri, Mohammad Mehdi Babaei, Edris Amirkabir Univ Technol Dept Ind Engn Tehran Iran Tarbiat Modares Univ Dept Ind Engn Tehran *** Iran

Portfolio optimization context has shed only a little light on the dependence structure among the financial returns along with the fat-tailed distribution associated with them. This study tries to find a remedy for this shortcoming by exploiting stable distributions as the marginal distributions together with the dependence structure based on copula function. We formulate the portfolio optimization problem as a multi-objective mixed integer programming. Value-at-Risk (VaR) is specified as the risk measure due to its intuitive appeal and importance in financial regulations. In order to enhance the model's applicability, we take into account cardinality and quantity constraints in the model. Imposing such practical constraints has resulted in a non-continuous feasible region. Hence, we propose two variants of multi-objective particle swarm optimization (MOPSO) algorithms to tackle this issue. Finally, a comparative study among the proposed MOPSOs, NSGAII and SPEA2 algorithms is made to demonstrate which algorithm is outperformed. The empirical results reveal that one of the proposed MOPSOs is superior over the other salient algorithms in terms of performance metrics. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Metaheuristics Portfolio optimization Stable distribution Copula function multi-objective particle swarm optimization

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Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2015年第6期18卷 1737-1757页

作者： Ramezani, Fahimeh Lu, Jie Taheri, Javid Hussain, Farookh Khadeer Univ Technol Sydney Decis Support & E Serv Intelligence Lab Ctr Quantum Computat & Intelligent Syst Sch SoftwareFac Engn & Informat Technol Sydney NSW 2007 Australia Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia

Optimizing task scheduling in a distributed heterogeneous computing environment, which is a nonlinear multi-objective NP-hard problem, plays a critical role in decreasing service response time and cost, and boosting Quality of Service (QoS). This paper, considers four conflicting objectives, namely minimizing task transfer time, task execution cost, power consumption, and task queue length, to develop a comprehensive multi-objective optimization model for task scheduling. This model reduces costs from both the customer and provider perspectives by considering execution and power cost. We evaluate our model by applying two multi-objective evolutionary algorithms, namely multi-objective particle swarm optimization (MOPSO) and multi-objective Genetic Algorithm (MOGA). To implement the proposed model, we extend the Cloudsim toolkit by using MOPSO and MOGA as its task scheduling algorithms which determine the optimal task arrangement among VMs. The simulation results show that the proposed multi-objective model finds optimal trade-off solutions amongst the four conflicting objectives, which significantly reduces the job response time and makespan. This model not only increases QoS but also decreases the cost to providers. From our experimentation results, we find that MOPSO is a faster and more accurate evolutionary algorithm than MOGA for solving such problems.

关键词： Cloud computing Task scheduling multi-objective particle swarm optimization multi-objective genetic algorithm Jswarm Cloudsim

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multi-objective optimal placement of protective devices on microgrid using improved binary multi-objective PSO

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第11期25卷 2621-2638页

作者： Prommee, Witoon Ongsakul, Weerakorn Rajamangala Univ Technol Lanna Fac Engn Elect Engn Field Study Chiang Rai 57120 Thailand Asian Inst Technol Sch Environm Resources & Dev Energy Field Study Klongluang 12120 Pathumthani Thailand

This paper proposes an improved binary multi-objectives particle swarm optimization (IB-MOPSO) for solving optimal multiple protective devices placement using improved reliability model in a microgrid (MG) system. The multi-objectives are to minimize system average interruption frequency index, system average interruption duration index, and total cost including investment and interruption cost. Binary multi-objectives PSO (B-MOPSO) is enhanced by using a bell shape function and three particle movement strategies including global guidance located in the least crowded areas, perturbation with different evolution method, and coverage of unexplored search space in the non-dominated front. For MG planning, IB-MOPSO can find better locations and number of protective devices including reclosers, switches, and fuses than B-MOPSO and ant colony optimization on the 51 section Provincial Electricity Authority of Thailand MG distribution system. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： multi-objective particle swarm optimization microgrid reliability

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A hybrid one-class rule learning approach based on swarm intelligence for software fault prediction

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INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2015年第4期11卷 289-301页

作者： Abdi, Yousef Parsa, Saeed Seyfari, Yousef Islamic Azad Univ Shabestar Sch Comp Engn East Azarbaijan Iran Iran Univ Sci & Technol Sch Comp Engn Tehran Iran Univ Tabriz Sch Comp Sci Tabriz Iran

Software testing is a fundamental activity in the software development process aimed to determine the quality of software. To reduce the effort and cost of this process, defect prediction methods can be used to determine fault-prone software modules through software metrics to focus testing activities on them. Because of model interpretation and easily used by programmers and testers some recent studies presented classification rules to make prediction models. This study presents a rule-based prediction approach based on kernel k-means clustering algorithm and Distance based multi-objective particle swarm optimization (DSMOPSO). Because of discrete search space, we modified this algorithm and named it DSMOPSO-D. We prevent best global rules to dominate local rules by dividing the search space with kernel k-means algorithm and by taking different approaches for imbalanced and balanced clusters, we solved imbalanced data set problem. The presented model performance was evaluated by four publicly available data sets from the PROMISE repository and compared with other machine learning and rule learning algorithms. The obtained results demonstrate that our model presents very good performance, especially in large data sets.

关键词： Fault prediction multi-objective particle swarm optimization Imbalanced data sets Classification rules Kernel k-means DSMOPSO-D

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Improving Generalization of Radial Basis Function Network with Adaptive multi-objective particle swarm optimization

Improving Generalization of Radial Basis Function Network wi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qasem, Sultan Noman Shamsuddin, Siti Mariyarn Hj Univ Teknol Malaysia Soft Comp Res Grp Fac Comp Sci & Informat Syst Skudai 81300 Johor Malaysia Univ Teknol Malaysia Soft Comp Res Grp K Econ Res Alliance Skudai 81300 Johor Malaysia

ISBN: (纸本)9781424427932

In this paper, an adaptive evolutionary multi-objective selection method of REF Networks structure is discussed. The candidates of REF Network structures are encoded into particles in particle swarm optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. The problem of unsupervised and supervised learning is discussed with Adaptive multi-objective PSO (AMOPSO). This study suggests an approach of REF Network training through simultaneous optimization of architectures and weights with Adaptive PSO-based multi-objective algorithm. Our goal is to determine whether Adaptive multi-objective PSO can train REF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that our proposed method provides an effective means for training REF Networks that is competitive with PSO-based multi-objective algorithm.

关键词： Radial basis function network Adaptive multi-objective particle swarm optimization multi-objective particle swarm optimization

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Study on a new method to eliminate the common-mode voltage based on improved SHEPWM 10

Study on a new method to eliminate the common-mode voltage b...

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10th IEEE Conference on Industrial Electronics and Applications

作者： Dai, Jing Li, Guoli Hu, Cungang Anhui Univ Sch Elect Engn & Automat Hefei 230039 Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Techn Hefei 230039 Peoples R China

ISBN: (纸本)9781479983896

Reducing the common-mode voltage of three-level active neutral point clamped (ANPC) inverter is of great significance in high-voltage and high-power applications. Using the improved specific harmonic elimination pulse width modulation (SHEPWM) combined with multi-objective particle swarm optimization to eliminate the common-mode component of output voltage of the inverter. The simulation result shows that this method is effective.

关键词： multi-objective particle swarm optimization SHEPWM common-mode voltage ANPC

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VPP Energy Resources Management considering Emissions: The case of northern Portugal 2020 to 2050

VPP Energy Resources Management considering Emissions: The c...

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IEEE Symposium Series Computational Intelligence

作者： Soares, Joao Borges, Nuno Lobo, Cristina Vale, Zita Polytech Porto ISEP IPP GECAD Knowledge Engn & Decis Support Res Ctr Rua Dr Antonio Almeida 431 P-4200072 Oporto Portugal

ISBN: (纸本)9781479975600

In SGs context, Distributed Generation (DG) based on renewable sources represents an alternative paradigm of energy supply and the opportunity for significant reduction in CO2 emissions. However, strict emissions regulations might impact profit seeking Virtual Power Plants (VPP) operation. This paper addresses energy management at distribution level and evaluates if electricity emissions are worth to be considered in different time horizons. A realistic case study is developed for a chosen area of the northern region of Portugal, namely one part of the distribution grid from Vila Real managed by a VPP, with estimated penetration of Electric Vehicles (EV), several Distributed Generation (DG), Demand Response (DR) and Energy Storage Systems (ESS). The considered characteristics of the case study took into account several studies and the forecasts made in the literature. For 2030 it is expected an average CO2 grid emission of 50 kgCO(2)/MWh in Portugal. The repository-based multi-objective particle swarm optimization (MOPSO) is used to tackle the developed optimization problem. Three scenarios are evaluated for the profit seeking VPP in 2020, 2030 and 2050 perspectives.

关键词： Energy Resources Management multi-objective particle swarm optimization Virtual Power Plant Smart Grid

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