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2nd IEEE International Conference on Communications and Signal Processing (ICCSP)

作者： Achankunju, Merin Pushpalakshmi, R. Kumar, A. Vincent Antony PSNA Coll Engg & Tech Dept IT Dindigul Tamil Nadu India

ISBN: (纸本)9781467348669;9781467348652

Mobile ad hoc network is a dynamic wireless network of mobile nodes without any centralized control. Energy efficient routing is the most challenging task of MANET. Clustering in MANET gives energy efficient solution. In this paper we proposed a secure clustering algorithm using multi-objective particle swarm optimization technique. The algorithm uses three objective functions based on nodes QoS parameters: trust value, life time and available bandwidth. The main advantage of MOPSO is that it provides multiple solutions at a time through optimal Pareto front. The proposed approach is simulated with MATLAB and NS2. Here we have discussed the performance of the proposed approach by varying number of nodes and transmission range of nodes.

关键词： Mobile ad hoc network multi-objective particle swarm optimization clustering trust value fuzzy

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D²MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in objective and Solution Spaces

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EVOLUTIONARY COMPUTATION 2014年第1期22卷 47-77页

作者： Al Moubayed, N. Petrovski, A. McCall, J. Robert Gordon Univ Aberdeen AB25 1HG Scotland

This paper improves a recently developed multi-objective particle swarm optimizer that incorporates dominance with decomposition used in the context of multi-objective optimization. Decomposition simplifies a multi-objective problem (MOP) by transforming it to a set of aggregation problems, whereas dominance plays a major role in building the leaders' archive. introduces a new archiving technique that facilitates attaining better diversity and coverage in both objective and solution spaces. The improved method is evaluated on standard benchmarks including both constrained and unconstrained test problems, by comparing it with three state of the art multi-objective evolutionary algorithms: MOEA/D, OMOPSO, and dMOPSO. The comparison and analysis of the experimental results, supported by statistical tests, indicate that the proposed algorithm is highly competitive, efficient, and applicable to a wide range of multi-objective optimization problems.

关键词： multi-objective particle swarm optimization decomposition-based evolutionary algorithms dominance archiving technique OMOPSO MOEA D

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Planning of Renewable DGs for Distribution Network Considering Load Model: A multi-objective Approach

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Energy Procedia 2014年 54卷 85-96页

作者： Partha Kayal Tanushree Bhattacharjee Chandan Kumar Chanda Department of Electrical Engineering Future Institute of Engineering and Management Kolkata-700150 India Department of Electrical Engineering Bengal Engineering and Science University Shibpur Howrah-711103 India

Renewable distributed generation (DG) has become one of the key drivers of fast changing energy scenario around the world. A clear strategy is ardently essential to accelerate growth of grid interactive renewable DGs. This paper proposes a constrained multi objective particle swarm optimization based planning of renewable DGs for distribution network. The proposed method tries to find the best trade-off between benefit to economic ratio, voltage stability factor and network security index and then generate optimal location, types and size of renewable DGs for distribution network. Effectiveness of the proposed methodology is shown on a typical 28-bus Indian distribution network. Test results depict that proper selection of locations, types and sizes of DGs would be helpful to maximize economical benefit, improve security and enhance voltage stability of the distribution network. The study also illustrates the effect of load modelling in planning of renewable DGs for distribution network.

关键词： multi-objective particle swarm optimization benefit to cost ratio voltage stability index network security index

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Application of multi-objective PSO Algorithm for Static Reactive Power Dispatch

Application of Multi-Objective PSO Algorithm for Static Reac...

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International Renewable Energy Congress

作者： Chefai DHIFAOUI Yosra WELHAZI Tawfik GUESMI Hsan HADJ ABDALLAH Control & Energies Management (CEM-Lab) National Engineering School of Sfax ENIS Sfax Tunisia

ISBN: (纸本)9781479921973

This paper presents the application of multiple-objective particle swarm optimization (MOPSO) algorithm for solving the static reactive power dispatch (RPD) problem. The optimal RPD problem is a nonlinear multi-objective optimization problem which involves the simultaneous minimization of two objective functions. The first function is the total real power losses in transmission lines. While the second one is the voltage deviation at load buses. This optimization is done by considering functional equality and inequality constraints. Since the problem is treated as a true multi-objective optimization problem, different trade-off solutions are provided. The decision maker has an option to choose a solution among the different trade-off solutions provided in the pareto-optimal front. The three-machine nine-bus system is used and the results show the effectiveness of MOPSO and confirm its potential to solve the multi-objective RPD problem.

关键词： multi-objective particle swarm optimization reactive power dispatch multi-objective optimization

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Robust Design of multimachine Power System Stabilizers Using multi-objective PSO Algorithm

Robust Design of Multimachine Power System Stabilizers Using...

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International Renewable Energy Congress

作者： Yosra WELHAZI Chefai DHIFAOUI Tawfik GUESMI Hsan HADJ ABDALLAH Control & Energies Management (CEM-Lab) National Engineering School of Sfax ENIS Sfax Tunisia

ISBN: (纸本)9781479921973

In this paper, robust design of multimachine power system stabilizers (PSSs) using multi-objective particle swarm optimization (MOPSO) is presented. The problem of selecting the stabilizer parameters is converted to an optimization problem with integral square error (ISE) and integral of time multiplied absolute value of the error (ITAE)-based objective functions. The MOPSO is employed to search for optimal PSS parameters for a wide range of operating conditions. The performance of the proposed MOPSO based PSSs is investigated for a three-machine nine-bus system under different configurations. The effectiveness of the proposed approach in enhancing the dynamic stability of power systems is confirmed through eigenvalue analysis and nonlinear simulation results.

关键词： robust power system stabilizers multimachine power system multi-objective particle swarm optimization dynamic stability

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Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2013年 128卷 798-806页

作者： Taheri, M. Moghaddam, M. R. Alavi Arami, M. Amirkabir Univ Technol Dept Civil & Environm Engn Tehran 158754413 Iran Amirkabir Univ Technol Dept Text Engn Tehran 158754413 Iran

In this research, Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were applied for optimization of Reactive Blue 19 removal using combined electro-coagulation/coagulation process through multi-objective particle swarm optimization (MOPSO). By applying RSM, the effects of five independent parameters including applied current, reaction time, initial dye concentration, initial pH and dosage of Poly Aluminum Chloride were studied. According to the RSM results, all the independent parameters are equally important in dye removal efficiency. In addition, ANFIS was applied for dye removal efficiency and operating costs modeling. High R-2 values (>= 85%) indicate that the predictions of RSM and ANFIS models are acceptable for both responses. ANFIS was also used in MOPSO for finding the best techno-economical Reactive Blue 19 elimination conditions according to RSM design. Through MOPSO and the selected ANFIS model, Minimum and maximum values of 58.27% and 99.67% dye removal efficiencies were obtained, respectively. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Electrocoagulation Chemical coagulation Response Surface Methodology Adaptive Neuro Fuzzy Inference System multi-objective particle swarm optimization Reactive Blue 19

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Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2013年第7期27卷 2143-2151页

作者： Majumder, Arindam Natl Inst Technol Dept Mech Engn Agartala 799055 Tripura India

The present contribution describes an application of a hybrid approach using fuzzy logic and particle swarm optimization (PSO) for optimizing the process parameters in the electric discharge machining (EDM) of AISI 316LN Stainless Steel. In this study, each experimentation was performed under different machining conditions of pulse current, pulse on-time, and pulse off-time. Machining performances such as MRR and EWR were evaluated. A Taguchi L9 orthogonal array was produced to plan the experimentation and the regression method was applied to model the relationship between the input factors and responses. A fuzzy model was employed to provide a fitness function to PSO by unifying the multiple responses. Finally, PSO was used to predict the optimal process parametric settings for the multi-performance optimization of the EDM operation. The experimental results confirm the feasibility of the strategy and are in good agreement with the predicted results over a wide range of machining conditions employed in the process.

关键词： Electric discharge machining multiple least-square regression technique Analysis of variance (ANOVA) Fuzzy logic multi-objective particle swarm optimization

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A new approach for optimum DG placement and sizing based on voltage stability maximization and minimization of power losses

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ENERGY CONVERSION AND MANAGEMENT 2013年 70卷 202-210页

作者： Aman, M. M. Jasmon, G. B. Bakar, A. H. A. Mokhlis, H. Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Malaya UMPEDAC Level 4 Wisma R&D UM Kuala Lumpur 59990 Malaysia

Distributed Generation (DG) placement on the basis of minimization of losses and maximization of system voltage stability are two different approaches, discussed in research. In the new proposed algorithm, a multi-objective approach is used to combine the both approaches together. Minimization of power losses and maximization of voltage stability due to finding weakest voltage bus as well as due to weakest link in the system are considered in the fitness function. particle swarm optimization (PSO) algorithm is used in this paper to solve the multi-objective problem. This paper will also compare the propose method with existing DG placement methods. From results, the proposed method is found more advantageous than the previous work in terms of voltage profile improvement, maximization of system loadability, reduction in power system losses and maximization of bus and line voltage stability. The results are validated on 12-bus, 30-bus, 33-bus and 69-bus radial distribution networks and also discussed in detailed. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Distributed generation Voltage stability indices multi-objective particle swarm optimization

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multi-objective Clearing of Reactive Power Market Including Plug-in Hybrid Electric Vehicle

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2013年第2期41卷 197-220页

作者： Farahani, H. Feshki Shayanfar, H. A. Ghazizadeh, M. S. Islamic Azad Univ Dept Elect Engn Sci & Res Branch Tehran *** Iran Iran Univ Sci & Technol Ctr Excellence Power Syst Automat & Operat Dept Elect Engn Tehran Iran Power & Water Univ Technol Tehran Iran

In this article, a multi-objective framework is proposed for reactive power market clearing with the presence of plug-in hybrid electric vehicles. The proposed multi-objective framework can concurrently optimize competing objective functions, including the total payment function of plug-in hybrid electric vehicles and generators and total grid losses. The total payment function is the payment paid to the plug-in hybrid electric vehicles and generators for their reactive power compensation. To calculate the vehicle's payment, the capability curve of the plug-in hybrid electric vehicle is first extracted, and after that, the expected payment function of the vehicle is proposed based on its capability curve. The problem of reactive power market clearing is solved by implementing multi-objective particle swarm optimization. The best compromise solution is chosen according to the specific preference among various non-dominated (Pareto optimal) solutions on a fuzzy approach basis. The effectiveness of the proposed method is examined based on a typical 17-node microgrid test system.

关键词： plug-in hybrid electric vehicle reactive power market total payment function multi-objective particle swarm optimization plug-in hybrid electric vehicle capability curve

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A multi-objective Service Selection Algorithm for Service Composition

A Multi-objective Service Selection Algorithm for Service Co...

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19th Asia-Pacific Conference on Communications (APCC)

作者： Liao, Jianxin Liu, Yang Zhu, Xiaomin Wang, Jingyu Qi, Qi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China EB Informat Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781467360500

Service composition is an efficient way to implement a service of complex business process. In service composition, service selection considering multiple QoS objectives is a challenge. Existing methods mainly utilize fitness function or constraint technique to convert multiple objectives service composition problems to single objective ones. These methods take effect with priori knowledge of problem's solution space. Besides, only one solution can be obtained in each execution of one existing method. Users can hardly acquire evenly distributed solutions with acceptable computation cost. In this paper, we propose a lightweight particle swarm optimization service selection algorithm using approximate distance and external archive mechanisms. Simulation results demonstrate that the proposed algorithm is more effective and efficient than the compared algorithm for multi-objective service composition problems.

关键词： multi-objective particle swarm optimization multi-objective service composition service selection

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