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IEEE International Conference on Industrial Technology (ICIT)

作者： Braiwish, N. Y. Anayi, F. J. Fahmy, A. A. Eldukhri, E. E. Cardiff Univ Sch Engn Wolfson Ctr Magnet Cardiff CF10 3AX S Glam Wales Cardiff Univ Inst Mech & Mfg Engn Cardiff CF10 3AX S Glam Wales

ISBN: (纸本)9781479978007

Electric machines designs for traction applications are concerned with high power density, high efficiency and low manufacturing cost. In order to achieve better combination of parameters that satisfy the machine electromagnetics, mechanical, and thermal designs limitations;an optimization algorithm is very necessary. Therefore;this work focuses on comparing two optimization algorithms that are utilized to search for optimal solution to design Brushless Permanent Magnet (BLPM) Motor. The target machine design has to meet with FReedomCAR specifications as detailed by UQM Technologies Inc. In this paper, a relatively new modern evolutionary optimization technique known as the Bees Algorithm (BA) employed to search for the optimum design parameters according to a pre-defined multiobjective function. The results from BA have been compared with the other results obtained from Genetic Algorithm (GA). Therefore, both optimization methods have been compared in terms of computational efficiency and ability to find optimal solution for BLPM. However, the comparison results show that BA has better computational ability to search for optimal parameters and achieves lower average value for the objective function than the GA.

关键词： Bees Algorithm Brushless Permanent Magnet Motor BLPM Genetic Algorithm fitness value multi-objective function

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PSO-based Protection Coordination and Power-loss Minimization in Distribution Systems with DG Sources using Optimal Fault Current Limiters 21

PSO-based Protection Coordination and Power-loss Minimizatio...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abdel-Salam, Mazen M. El-Mohandes, Mohamed Th Osman, Amira H. Assiut Univ Elect Engn Dept Assiut Egypt

ISBN: (纸本)9781728152899

Accommodation of DG sources in a radial distribution system may result in reversal of current flow in some feeder sections of the system with a subsequent protection mis-coordination. To maintain the protection coordination, DG sources are connected to the distribution system through fault current limiters (FCLs) whose impedances are unknowns. A method is proposed to formulate an optimization function to minimize power-loss and to ensure that the fault currents restore to their original values without DG sources. The proposed method is based on two approaches. In the two-stage approach, the sizes and locations of DG sources are determined in the first-stage followed by the determination of sizes of FCLs in the second-stage. In the single-stage approach, the sizes of FCLs as well as the sizes and locations of DG sources are determined simultaneously. Minimization of the objective function is achieved using Particle Swarm Optimization (PSO) technique to obtain the optimum impedance values of the FCLs in addition to the sizes and locations of DG sources. The method is applied to the Canadian benchmark 9-bus and the IEEE 33-/69-bus distribution systems. The obtained impedances of the FCLs as well as the sizes and locations of DG sources are correlated with those reported in the literature for the same distribution system.

关键词： Distributed Generation fault current-limiter single-objective function particle-swarm optimization multi-objective function

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An effective approach to support multi-objective optimization in Software reliability allocation for improving quality 1

An effective approach to support multi-objective optimizatio...

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International conference on Graph Algorithms, High Performance Implementations and its Applications (ICGHIA)

作者： Sangeetha, M. Arumugam, C. Kumar, K. M. Senthil Shankar, S. Hari CIT Dept Comp Sci & Engn Coimbatore 641014 Tamil Nadu India CIT Dept Mech Engn Coimbatore 641014 Tamil Nadu India KCT Mech Engn Coimbatore 641035 Tamil Nadu India

Software development life cycle involves requirement analysis, design, coding, testing and implementation. Software Quality is the most important one, since the success of a Software Engineer relies on the development of failure free software. One of the main quality factors is reliability. Reliability can improve through Software Reliability Models, analyze of reliability data, proper utilization of debuggers, internal quality factors and evaluating the measurement results. Currently, there are several software reliability allocation techniques, but schedule planning and effective resource use are unavailable because they do not support multi-objective optimization. It is an effective software reliability allocation technique that supports multi objective optimization. This approach assists schedule planning as well as effective resources allocation. This paper can aid to increase the effectiveness of resource uses and to establish schedule planning. It will lead to improve Software Reliability. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Reliability Fault Failure Rate Genetic Algorithm multi-objective function

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Optimization of Machining Process for Aluminum Alloy Thin-walled Supporting Part on UG NX

Optimization of Machining Process for Aluminum Alloy Thin-wa...

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International Conference on Material and Manufacturing Technology

作者： Hao Yishu Zhang Baogang Cui Xu Tongji Univ Coll Mech Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9780878492435

According to the structural features of the supporting part, the manufacturing process plan of it is made and the fixtures are designed. The process parameters are optimized by the means of setting up multi-objective function that cover productivity, cost of production. processing efficiency;The NC Programs of supporting part are drawn with UG CAM and the machining tool-path is optimized through machining simulation.

关键词： UG NX CAM/CAE Supporting part Optimization of process parameters multi-objective function

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multi-objective Cooperative Algorithms Based on Game Theory in Cognitive Radio

Multi-Objective Cooperative Algorithms Based on Game Theory ...

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International Conference on Wireless Communications and Signal Processing

作者： Ye Zhihui Shen Keqin Nanjing Univ Dept Elect Sci & Engn Nanjing 210008 Peoples R China PLA Univ Sci & Technol Inst Command Automat Nanjing Peoples R China

ISBN: (纸本)9781424448562

A multi-objective function cooperative algorithm based on non-collaboration game and Nash equilibrium is designed, which combines Maximum Bandwidth Profitability with Maximum Proportional Fairness. As applied in cognitive radio environment, the proposed algorithm can win trade-off between fairness and bandwidth profit, and realize effective spectrum distribution.

关键词： Game theory multi-objective function Cognitive radio Fairness Bandwidth profitability

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Enhancement of network coding in wireless sensor network using improved lion algorithm: intention toward maximizing network throughput and lifetime

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INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2022年第4期10卷 444-467页

作者： Dike, Prashant R. Vishwanath, T. S. Rohakale, Vandana Bheemanna Khandre Inst Technol Bidar India Sinhgad Inst Technol & Sci Lonavala Pune Maharashtra India

Purpose Since communication usually accounts as the foremost problem for power consumption, there are some approaches, such as topology control and network coding (NC), for diminishing the activity of sensors' transceivers. If such approaches are employed simultaneously, then the overall performance does raise as expected. In a wireless sensor network (WSN), the linear NC has been shown to enhance the performance of network throughput and reduce delay. However, the NC condition of existing NC-aware routings may experience the issue of false-coding effect in some scenarios and usually neglect node energy, which highly affects the energy efficiency performance. The purpose of this paper is to propose a new NC scheduling in a WSN with the intention of maximizing the throughput and minimizing the energy consumption of the network. Design/methodology/approach The improved meta-heuristic algorithm called the improved mutation-based lion algorithm (IM-LA) is used to solve the problem of NC scheduling in a WSN. The main intention of implementing improved optimization is to maximize the throughput and minimize the energy consumption of the network during the transmission from the source to the destination node. The parameters like topology and time slots are taken for optimizing in order to obtain the concerned objective function. While solving the current optimization problem, it has considered a few constraints like timeshare constraint, data-flow constraint and domain constraint. Thus, the network performance is proved to be enhanced by the proposed model when compared to the conventional model. Findings When 20 nodes are fixed for the convergence analysis, performed in terms of multi-objective function, it is noted that during the 400th iteration, the proposed IM-LA was 10.34, 13.91 and 50% better than gray wolf algorithm (GWO), firefly algorithm (FF) and particle swarm optimization (PSO), respectively, and same as LA. Therefore, it is concluded that the proposed IM-LA p

关键词： Wireless sensor network Network coding multi-objective function Energy consumption Throughput Improved mutation-based lion algorithm Network constraints

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Comparative Analysis of Colliding Bodies Optimization-based Inventory Management over other Heuristics: Cloud with Blockchain Criteria 11

Comparative Analysis of Colliding Bodies Optimization-based ...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Govindasamy, C. Antonidoss, A. Hindustan Inst Technol & Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665414517

The main intention of this paper is to develop inventory management in a secured manner based on the adoption of blockchain under the cloud sector using Colliding Bodies Optimization (CBO). For accomplishing that, a multi-objective function ismodeled based on transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, back-ordering cost and quality improvement cost, procurement cost, andstockout cost. To solve this multi-objective problem, this proposal makes a valuable comparative analysis by four different heuristic methods like FireFly (FF), Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), and Dragonfly Algorithm (DA). For validating the proposed CBO with other heuristic-based inventory management models, a comparative analysis is done based on diverse aspects.

关键词： Inventory Management Colliding Bodies Optimization Cloud Sector BlockChain Technology multi-objective function

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Optimal Power Quality Monitor Placement in Power Systems Based on Particle Swarm Optimization and Artificial Immune System

Optimal Power Quality Monitor Placement in Power Systems Bas...

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3rd Conference on Data Mining and Optimization (DMO)/1st multi Conference on Artificial Intelligence Technology (MCAIT)

作者： Ibrahim, A. A. Mohamed, A. Shareef, H. Ghoshal, S. P. Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Nat Inst Technol Dept Elect Engn Durgapur 713209 West Bengal India

ISBN: (纸本)9781612842127

In the modern industry, most of the equipment use semiconductor devices and microprocessors which are sensitive against power disturbances. Among power disturbances, voltage sags are considered as the most frequent type of disturbances in the field and their impact on sensitive loads is severe. However, to assess voltage sags, installation of power quality monitors (PQM) at all system buses is not economical. Thus, this study is carried out to develop a power quality monitor positioning algorithm to find the optimal number and placement of PQMs in both transmission and distribution systems. In this approach, first, the concept of topological monitor reach area is introduced. Then the binary particle swarm optimization hybridized with artificial immune system is used to solve multi-objective function in finding the optimal placement of PQMs. The proposed algorithm has been implemented and tested on the IEEE 30-bus and the 69-bus test systems to show the effectiveness of the proposed method for both transmission and distribution systems.

关键词： voltage sag power quality monitor binary particle swarm optimization artificial immune system multi-objective function

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multi-objective Service Similarity Metrics for more Effective Service Engineering Methods 8

Multi-Objective Service Similarity Metrics for more Effectiv...

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IEEE 8th International Conference on Service-Oriented Computing and Applications

作者： Athanasopoulos, Dionysis Zarras, Apostolos V. Politecn Milan Elect Informat & Bioengn Dept Milan Italy Univ Ioannina Comp Sci & Engn Dept GR-45110 Ioannina Greece

ISBN: (纸本)9781467394697

The usage of single-objective similarity functions in engineering tasks of service-oriented software may reduce their effectiveness, since a single similarity value can be misleading. In particular, a single similarity value cannot be clearly interpreted, since it hides the values of its individual objectives. The state-of-the-art approaches, which use service similarity functions, typically rely on single-objective functions. Going in a complementary direction, we propose the usage of multi-objective functions for calculating service similarity. We formally define such a function and we provide emerging results, which show that the effectiveness of service engineering tasks can be improved by using multi-objective functions.

关键词： Service-oriented engineering service interface service similarity function multi-objective function

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Optimal Sizing and Locations of DG Sources in Distribution Systems: A Review of Different Techniques 21

Optimal Sizing and Locations of DG Sources in Distribution S...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abdel-Salam, Mazen El-Mohandes, Mohamed Th Yousef, Ali M. Ramadan, Reham Assiut Univ Dept Elect Engn Assiut Egypt

ISBN: (纸本)9781728152899

This paper is aimed at presenting the importance of DG sources in distribution systems and reviewing the published work on optimal planning through determination of optimal locations and sizes of DG sources by minimizing single-objective function (SOF) and multi-objective function (MOF). The indices of the SOF include active power loss, reactive power loss, bus voltage deviation, voltage stability, cost and others. The MOF combines several indices through weighting coefficients depending on the importance of the indices. The optimization techniques are reviewed after being classified into conventional and artificial-intelligence based-ones. Particular attention is directed to genetic algorithm and its hybrids with other techniques for optimal sizing and location of DG sources.

关键词： genetic algorithm distributed generation artificial-intelligence single-objective function multi-objective function

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