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4th IEEE International Conference on Cloud Networking (CloudNet)

作者： Portaluri, Giuseppe Giordano, Stefano Univ Pisa I-56100 Pisa Italy

ISBN: (纸本)9781467395014

In this paper we present a new task allocator for Cloud Data Center (DC). The implementation is based on two different heuristics: multi-objective genetic algorithms (MOGA) and Simulated Annealing (SA). The allocator reduces at the same time both task completion time and server and switches power consumption, avoiding network link congestion. The evaluation results show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers with a quadratic time complexity for MOGA and a linear time complexity for SA.

关键词： Data center Cloud Computing multi-objective genetic algorithm Simulated Annealing Resource Allocation Power Efficiency

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Optimal corrective actions for power systems using multi-objective genetic algorithms

Optimal corrective actions for power systems using multi-obj...

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42nd International Universities Power Engineering Conference

作者： El Ela, Adel A. Abou El-Din, Ashraf Zin Spea, Shaimaa R. Minofiya Univ Dept Elect Engn Fac Engn Menoufia Egypt

ISBN: (纸本)9781905593361

In this paper, optimal corrective control actions are presented to restore the secure operation of power system for different operating conditions. genetic algorithm (GA) is one of the modern optimization techniques, which has been successfully applied in various areas in power systems. Most of the corrective control actions involve simultaneous optimization of several objective functions, which are competing and conflicting each other. The multi-objective genetic algorithm (MOGA) is used to optimize. the corrective control actions. Three different procedures based on GA and MOGA are proposed to alleviate the violations of the overloaded lines and minimize the transmission line losses. The first procedure is based on corrective switching of the transmission lines and generation re-dispatch. The second procedure is carried out to determine the optimal siting and sizing of distributed generation (DG). While, the third procedure is concerned into solving the generation-load imbalance problem using load shedding. Numerical simulations are carried out on two test systems in order to examine the validity of the proposed procedures.

关键词： corrective control actions multi-objective genetic algorithm generation re-dispatch distributed generation load shedding

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An Improved genetic algorithm for multi-objective Optimization

An Improved Genetic Algorithm for Multi-objective Optimizati...

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智能计算及其应用国际会议

作者： CHEN Guolong1,2, GUO Wenzhong1, TU Xuezhu1, CHEN Huowang2 (1. Institute of Mathematics and Computer Science, Fuzhou University, Fuzhou 350002 , China) (2. School of Computer Science, National University of Defense Technology, Changsha 410073, China)

This paper describes a non-generational GA for multi-objective optimization problems (MOP) based on a crossover operator called DC (Dislocation Crossover). In it the replacement policy is such that an

关键词： multi-objective genetic algorithm dislocation crossover multi-objective optimization non-dominated solutions

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Partially Optimized Cyclic Shift Crossover for multi-objective genetic algorithms for the multi-objective Vehicle Routing Problem with Time-Windows

Partially Optimized Cyclic Shift Crossover for Multi-Objecti...

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2014 IEEE Symposium on Computational Intelligence in multi-Criteria Decision-Making (MCDM)

作者： Pierre, Djamalladine Mahamat Zakaria, Nordin Univ Teknol PETRONAS Ctr High Performance Comp Perak Malaysia

ISBN: (纸本)9781479944675

The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with multi-objective genetic algorithms (MOGAs). MOGAs, thanks to its genetic operators such as selection, crossover, and/or mutation, constantly modify a population of solutions in order to find optimal solutions. However, given the complexity of VRPs, conventional crossover operators have major drawbacks. The Best Cost Route Crossover is lately gaining popularity in solving multi-objective VRPs. It employs a brute force approach to generate new children. Such approach may be unacceptable when presented with a relatively large problem instance. In this paper, we introduce a new crossover operator, called Partially Optimized Cyclic Shift Crossover (POCSX). A comparative study, between a MOGA based on POCSX, and a MOGA which is based on the Best Cost Route Crossover affirms the level of competitiveness of the former.

关键词： multi-objective Vehicle Routing Problem multi-objective genetic algorithm Crossover Mutation

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A new method to construct the non-dominated set in multi-objective genetic algorithms

A new method to construct the non-dominated set in multi-obj...

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2nd International Conference on Intelligent Information Processing

作者： Zheng, JH Shi, ZZ Ling, CX Xie, Y Xiangtan Univ Coll Informat Engn Xiangtan 411105 Peoples R China

ISBN: (纸本)038723151X

There have been widespread applications for multi objective genetic algorithm (MOGA) on highly complicated optimization tasks in discontinuous, multi-modal, and noisy domains. Because the convergence of MOGA can be reached with the non-dominated set approximating the Pareto Optimal front, it is very important to construct the non-dominated set of MOGA efficiently. This paper proposes a new method called Dealer's Principle to construct non-dominated sets of MOGA, and the time complexity is analyzed. Then we design a new MOGA with the Dealer's Principle and a clustering algorithm based on the core distance of clusters to keep the diversity of solutions. We show that our algorithm is more efficient than the previous algorithms, and that it produces a wide variety of solutions. We also discuss the convergence and the diversity of our MOGA in experiments with benchmark optimization problems of three objectives.

关键词： multi-objective genetic algorithm multi-objective optimum dominated relationship non-dominated set

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multi-objective optimization of PID controller for temperature control in centrifugal machines using genetic algorithm

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Research Journal of Applied Sciences, Engineering and Technology 2014年第9期7卷 1794-1802页

作者： Singh, Sanjay Kr. Boolchandani, D. Modani, S.G. Katal, Nitish Department of ECE Anand International College of Engineering Jaipur Rajasthan 303012 India Malaviya National Institute of Technology Jaipur Rajasthan 302017 India Department of Electronics and Communication Engineering Amity University Rajasthan 302001 India

The main aim of the study focuses on the optimizing the response of the PID controllers used typically for temperature control loop in centrifugal machines in sugar industry using soft-computing. The centrifugal machines are used for the filtering sugar and molasses and the whole process is carried out at a certain fixed temperature. Any alterations from the set-point will cause instability in the system resulting in unsafe process conditions, poor product quality, unnecessary plant shutdowns, higher maintenance and operating costs, etc. This study employs the optimization of PID controllers using multi-objective genetic algorithm for better plant operations. For the initial tuning of the PID controllers, classical methods like Ziegler-Nichols, Chien-Hrones-Reswick (CHR) and robust time response have been used, but all have shown the transient response;so MATLAB's PID Tuner has been used for the initial estimation of the parameters followed by optimization using genetic algorithm and multi-objective genetic algorithm. On comparing the results, better results have been obtained in case of multi-objective genetic algorithm optimization offering better plant operation and process safety which classical methods have failed to provide. © Maxwell Scientific Organization, 2014.

关键词： Controller tuning genetic algorithm multi-objective genetic algorithm PID controllers PID optimization Robust time response Temperature control

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Modeling of solid oxide fuel cell (SOFC) electrodes from fabrication to operation: Microstructure optimization via artificial neural networks and multi-objective genetic algorithms

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ENERGY CONVERSION AND MANAGEMENT 2019年 198卷 111916-000页

作者： Yan, Z. He, A. Hara, S. Shikazono, N. Univ Tokyo Inst Ind Sci Meguro Ku Komaba 4-6-1 Tokyo 1538505 Japan Chiba Inst Technol Dept Mech Engn Fac Engn Tsudanuma 2-17-1 Narashino Chiba 2750016 Japan

In this study, a modeling framework is proposed for the optimization of the solid oxide fuel cell (SOFC) electrode microstructures. This involves sequential simulations of the SOFCs from initial powder to final electrochemical performance with artificial intelligence-assisted multi-objective optimization. The effects of starting powder parameters such as particle size, particle size distribution (PSD) and pore former content on cathodic overpotential and degradation rate of SOFCs are studied. It is shown that fine particle size and/or low pore former content lead to low cathodic overpotential but high degradation rate in the investigated range of the parameters. Predictive models for the cathode overpotential and degradation rate are established by an artificial neural network using the simulation data. The Sobol global sensitivity study suggests that particle size and pore former content play important roles in determination of the cathode overpotential and degradation rate while the PSD effect is insignificant. A multi-objective genetic algorithm (MOGA) is used to minimize both the overpotential and degradation rate of the cathode. The Pareto front is obtained for the optimal design of cathode microstructures. Compared to the grid search method, the MOGA proves to be more robust and efficient for SOFC electrode microstructure optimization.

关键词： Solid oxide fuel cells Microstructure optimization Artificial neural network Parameter sensitivity multi-objective genetic algorithm

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U-shaped assembly line balancing problem with genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第16期46卷 4637-4649页

作者： Hwang, Rea Kook Katayama, Hiroshi Gen, Mitsuo Waseda Univ Grad Sch Sci & Engn Shinjuku Ward Tokyo 1690072 Japan Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

This paper presents a multi-objective genetic algorithm (moGA) to solve the U-shaped assembly line balancing problem (UALBP). As a consequence of introducing the just-in-time (JIT) production principle, it has been recognized that U-shaped assembly line systems offer several benefits over the traditional straight line systems. We consider both the traditional straight line system and the U-shaped assembly line system, thus as an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload. The results of experiments show that the proposed model produced as good or even better line efficiency of workstation integration and improved the variation of workload.

关键词： U-shaped assembly line multi-objective genetic algorithm priority-based coding

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Performance prediction and geometry optimization of ejector in PEMFC system using coupled CFD-BPNN and genetic algorithm

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APPLIED THERMAL ENGINEERING 2024年 251卷

作者： Pang, Zihui Han, Jiquan Feng, Jianmei Diao, Anna Yao, Yanchen Peng, Xueyuan Xi An Jiao Tong Univ Sch Energy & Power Engn 28 Xianning West Rd Xian 710049 Peoples R China Shanghai Marine Diesel Engine Res Inst Shanghai 201108 Peoples R China Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Shaanxi Peoples R China

The ejector is a promising hydrogen recirculation device in proton exchange membrane fuel cell (PEMFC) systems. However, the limited entrainment performance of the ejector at wide operating conditions hinders its development and widespread application in PEMFC systems. To address this challenge and design a highperformance ejector adapted to the wide power range of PEMFC systems, a backpropagation neural network (BPNN) model with computational fluid dynamics (CFD) simulation data is developed. The sensitivity analysis of geometric parameters based on the CFD model reveals that the nozzle throat diameter and the mixing chamber diameter exert the most pronounced impact on the entrainment performance, with average influence rates of 24% and 57%, respectively. Furthermore, two advanced multi-objective genetic algorithms are applied to improve ejector performance. The linear weighted genetic algorithm (LWGA) method proves effective in elevating the overall ejector performance, achieving a remarkable enhancement in entrainment performance of up to 7.9%. On the other hand, the non-dominated sorting genetic algorithm (NSGA- II) method is favorable for expanding the operational power range of the ejector by 30%, as well as a 4.5% increase in overall ejector performance. This work provides a robust framework for designing and optimizing high-performance ejectors in high-power PEMFC systems.

关键词： Ejector multi-objective genetic algorithm Backpropagation neural network PEMFC

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genetic algorithms using multi-objectives in a multi-agent system

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ROBOTICS AND AUTONOMOUS SYSTEMS 2000年第2-3期33卷 179-190页

作者： Cardon, A Galinho, T Vacher, JP Univ Havre Inst Technol LIH F-76610 Le Havre France UPMC LIP6 Paris VI F-75252 Paris 6 France LIH Inst Super Etud Logist F-76063 Le Havre France

We are interested in a job-shop scheduling problem corresponding to an industrial problem. Gantt diagram's optimization can be considered as an NP-difficult problem. Determining an optimal solution is almost impossible, but trying to improve the current solution is a way of leading to a better allocation. The goal is to reduce the delay in an existing solution and to obtain better scheduling at the end of the planning. We propose an original solution based on genetic algorithms which allows to determine a set of good heuristics for a given benchmark. From these results, we propose a dynamic model based on the contract-net protocol. This model describes a way to obtain new schedulings with agent negotiations. We implement the agent paradigm on parallel machines. After a description of the problem and the genetic method we used, we present the benchmark calculations that have been performed on an SGI Origin 2000. The interpretation of these is a way to refine heuristics given by our evolution process and a way to constrain our agents based on the contract-net protocol. This dynamic model using agents is a way to simulate the behavior of entities that are going to collaborate to improve the Gantt diagram. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： job-shop scheduling problem multi-objective genetic algorithm multi-agent system contract-net protocol

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