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2013 IEEE 6th international workshop on Computational Intelligence and applications, IWCIA 2013

作者： Hayashida, Tomohiro Nishizaki, Ichiro Matsumoto, Tsubasa Hiroshima University 1-4-1 Kagamiyama Higashihiroshima Hiroshima Japan

ISBN: (纸本)9781467357265

In the traditional procedures, data classification with a high degree of accuracy by neural networks requires heuristic structural optimization by using expert knowledge. However, the optimization procedure takes an immense amount of time and effort. Additionally, high-dimensional data is difficult to classify for many analysts, thus, it would appears that accuracy of data classification grows higher by proper selection and dimensional compression of input data. This study suggests new procedure for data classification by using neural networks. For dimensional compression of input data, the suggested procedure uses sandglass type neural networks, and tabu search algorithms are applied for input data selection and structural optimization of union between a sandglass type and a feedforward neural networks. © 2013 IEEE.

关键词： Structural optimization

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A (μ, λ) evolutionary and particle swarm hybrid algorithm, with an application to dinosaur gait optimization

A (μ, λ) evolutionary and particle swarm hybrid algorithm,...

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2013 IEEE 6th international workshop on Computational Intelligence and applications, IWCIA 2013

作者： Matsumura, Yoshiyuki Kobayashi, Ayumu Sugiyama, Kiyotaka Pataky, Todd Yasuda, Toshiyuki Ohkura, Kazuhiro Sellers, Bill Shinshu University 3-15-1 Tokida Ueda Nagano 386-8567 Japan Hiroshima Universisty Kagamiyama Higashihiroshima Hiroshima 739-8527 Japan University of Manchester D1239 Michael Smith Building Oxford Road Manchester M13 9PT United Kingdom

ISBN: (纸本)9781467357265

A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for the numerical optimization problems. In order to find out the performance of the hybrid, the computer experiment is tested on dinosaur's gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster in the first phase. © 2013 IEEE.

关键词： Particle swarm optimization (PSO)

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A simple and fast particle swarm optimization and its application on portfolio selection

A simple and fast particle swarm optimization and its applic...

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2009 international workshop on Intelligent Systems and applications, ISA 2009

作者： Wang, Wenjun Wang, Hui Wu, Zhijian Dai, Hubei Department of Management Engineering Nanchang Institute of Technology Nanchang 330099 China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China

ISBN: (纸本)9781424438945

Particle Swarm optimization (PSO) has shown its good performance on well-known numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in diversity. Some diversity-guided PSO algorithms have proposed to maintain diversity, while these techniques cost much computation time on the calculation of diversity. In this paper, a simple and fast PSO (hybrid PSO, HPSO) is proposed, which indirectly maintains the diversity of swarm but not compute it. Experimental studies on 8 well-known benchmark functions and a portfolio selection optimization problem show that the HPSO does not only obtain better performance than the standard PSO and other two diversity-guided PSO algorithms, but almost cost the same computation time with the standard PSO. © 2009 IEEE.

关键词： Particle swarm optimization (PSO)

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Preface Special issue on ＂Modern optimization and applications＂

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Journal of Computational Mathematics 2018年第3期36卷 I0001-I0002页

作者： Xiaojun Chen Yuhong Dai Peter Richtarik Yinyu Ye The Hong Kong Polytechnic University The Chinese Academy of Sciences The University of Edinburgh Stanford University

This special issue contains eight selected papers from the international workshop on Modern optimization and applications, which was held over three days, 27-29 June 2016 at Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing. This conference brought together leading scientists, researchers, and practitioners from world to exchange and shared ideas and approaches in using modern optimization techniques to model and solve real-world application problems from engineering, industry, and management. A prominent feature of this conference is the mixture of optimization theory, optimization methods, and practice of mathematical optimization. This conference provided a forum for researchers from academy to present their latest theoretical results while practitioners from industry to describe their real-world applications and discuss with researchers the best way to construct suitable optimization models and how to find algorithms capable of solving these models.

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Firefly Algorithm and Grey Wolf Optimizer for Constrained Real-Parameter optimization

Firefly Algorithm and Grey Wolf Optimizer for Constrained Re...

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Joint World Congress of the international-Fuzzy-Systems-Assoc (IFSA) and the Annual Conference of the North-American-Fuzzy-Information-Proc-Soc (NAFIPS) / 12th international workshop on Constraint Programming and Decision Making (CoProd)

作者： Rodriguez, Luis Castillo, Oscar Garcia, Mario Soria, Jose Tijuana Inst Technol Tijuana BC Canada

ISBN: (纸本)9783030219208;9783030219192

The main goal of this paper is to present the performance of two popular algorithms, the first is the Firefly Algorithm (FA) and the second one is the Grey Wolf Optimizer (GWO) algorithm for complex problems. In this case the problems that we are presenting are of the CEC 2017 Competition on Constrained Real-Parameter optimization in order to realize a brief analysis, study and comparison between the FA and GWO algorithms respectively.

关键词： Grey Wolf Optimizer Firefly Algorithm Constraints Complex problems Study optimization

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Traffic intersection signal-planning multi-object optimization based on genetic algorithm

Traffic intersection signal-planning multi-object optimizati...

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2nd international workshop on Intelligent Systems and applications, ISA2010

作者： Zhang, Ben Shang, Lei Chen, Dan School of Energy and Power Engineering WuHan University of Technology Wuhan HuBei 430063 China Marine Engineering Institute JiMei University Xiamen FuJian 361021 China

ISBN: (纸本)9781424458745

For improving the intersection traffic capacity and reducing the vehicle emission, the solution that aim at the multi-object optimization was presented by using genetic algorithm (GA), and urban traffic microscopic simulation model (UTMSM) combined with GA was developed. The simulation was performed and the result indicated that the optimization method in this paper was effective to get better traffic signal control effects and to improve the environment. ©2010 IEEE.

关键词： Genetic algorithms

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Energy optimization in a HW/SW tool: Design of low power architecture system 3

Energy optimization in a HW/SW tool: Design of low power arc...

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3rd IEEE international workshop on System-on-Chip for Real-Time applications

作者： Guitton-Ouhamou, P Belleudy, C Auguin, M Signaux & Syst Sophia Antipolis Lab Informat F-06903 Sophia Antipolis France

ISBN: (纸本)076951944X

Minimizing power consumption in System On Chip is a crucial task. So the parameter of consumption has to be introduced in HW/SW tool. This paper describes how our HW/SW codesign tool, CODEF, is extended to have power consumption and optimization ability. Some strategies of consumption optimizations are presented First, we present how to build the library composed of consumption models of hardware and software modules (that take into account frequency and supply voltage). Then, we describe the algorithm that computes the peak power and the energy. To reduce the energy, we describe a strategy during allocation step to minimize energy. In this way, the partitioning algorithm has been modified and, we present some results of architectures optimization with some important gains of 50%...

关键词： Clocks Computer architecture Design optimization Energy consumption Frequency Hardware Partitioning algorithms Processor scheduling Software libraries System-on-a-chip

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Peak and valley time-of-use price optimization strategy with distributed power supply 8

Peak and valley time-of-use price optimization strategy with...

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8th international Symposium on Computational Intelligence and Industrial applications and 12th China-Japan international workshop on Information Technology and Control applications, ISCIIA and ITCA 2018

作者： Hu, Bo Zhang, Yu Han, Xin-Yang Jiao, Yu-Qiao Ding, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China State Grid Energy Research Institute Co. LTD. Beijing102200 China

This paper proposes a peak-to-valley time-based electricity price optimization method based on NSGA-II with distributed power supply. First, the curve-fitting method is used to obtain the comprehensive electricity price response curve of users in the whole society. Secondly, based on the use of time-sharing electricity price, the peak-to-valley time-sharing optimization model is established. Then, based on the NSGA-II optimization method, the optimal model is solved, and the optimal electricity price is obtained at different time periods to change the peak-to-valley load and the valley load. Finally, the simulation results verify the effectiveness of the proposed method. © ISCIIA and ITCA 2018 - 8th international Symposium on Computational Intelligence and Industrial applications and 12th China-Japan international workshop on Information Technology and Control Application.

关键词： Genetic algorithms

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A Review on Intelligent Scheduling and optimization for Flexible Job Shop

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international JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第10期21卷 3127-3150页

作者： Jiang, Bin Ma, Yajie Chen, Lijun Huang, Binda Huang, Yuying Guan, Li Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Dept Automat Control Nanjing 211106 Peoples R China AVIC Nanjing Engn Inst Aircraft Syst Nanjing 210016 Peoples R China AVIC Nanjing Engn Inst Aircraft Syst Dept Engn & Technol Nanjing 210016 Peoples R China

Flexible job shop scheduling problem is a NP-hard combinatorial optimization problem, which has significant applications in the field of workshop scheduling and intelligent manufacturing. Due to its complexity and significance, lots of attention have been paid to tackle this problem. This paper reviews some of the researches on this problem, by presenting and classifying the different criteria, constraints, and solution approaches. The existing solution methods for the flexible job shop scheduling problem in this literature are classified into exact algorithms, heuristics, and meta-heuristics, which are thoroughly reviewed. Particularly, the paper highlights the flexible job shop scheduling problem in the context of dynamic events and preventive maintenance. These dynamic events, such as machine breakdowns and unexpected changes in job requirements, present additional challenges to the scheduling problem. Furthermore, this paper analyzes the development trends in the manufacturing industry and summarizes detailed future research opportunities for the flexible job shop scheduling problem.

关键词： Dynamic scheduling flexible job shop scheduling intelligent optimization preventive maintenance static scheduling

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Optimized datapath design by evolutionary computation 3

Optimized datapath design by evolutionary computation

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3rd IEEE international workshop on System-on-Chip for Real-Time applications

作者： Araújo, SG Mesquita, AC Pedroza, ACP UFRJ Dept Elect Engn Rio De Janeiro Brazil

ISBN: (纸本)076951944X

High-level design entry tools offer a nice framework to deal with today's complex systems while shortening the design cycle. Nevertheless, such tools provide poor quality results both in area usage and timing performance issues. This paper presents a methodology to design optimized datapaths based on evolutionary techniques and HLS tools. VHDL descriptions of the system are automatically generated by Genetic Programming. To improve the design structural quality of such descriptions a two-stage, multiobjective optimization algorithm is used to insure both desired functionality and area constraints.

关键词： Circuits Design methodology Design optimization Evolutionary computation Genetic algorithms Genetic programming Hardware design languages High level synthesis Space exploration Timing

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