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international workshop on Intelligent Systems and applications, ISA

作者： Hui Zhao Rong Liu Zhuoqun Zhao Chuanli Fan School of Electrical Engineering Tianjin University of Technology Tianjin China School of Mechanical&Vehicle Engineering Hunan University Changsha China Datang Xiangyang Wind Power Company Limited China

This paper analyzed the enterprise process energy consumption systematically with a lot of statistic data starting from energy efficiency, and established the energy consumption prediction model based on genetic algorithm of wavelet neural network (GA-WNN). This paper made previous optimization training with genetic algorithm, which have feature of natural evolution regularity, to the weights and dilation-shift scale of wavelet neural network. Partly replaced gradient descent method of wavelet frame neural network where parameters optimization only with a single gradient direction, overcame the shortcoming that easily into the local minimum and cause oscillation effect of the single gradient descent method. Simulation results showed the effectiveness of the forecasting model, and it is feasible for solving the process energy consumption multi-factor quantitative analysis problem which general mathematical model is difficult to describe.

关键词： Artificial neural networks Energy consumption Genetic algorithms Wavelet transforms Predictive models Forecasting optimization

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Solving the workshop Layout optimization Problem Based on a Constrained Multimodal Multiobjective Evolutionary Algorithm

Solving the Workshop Layout Optimization Problem Based on a ...

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Data-driven optimization of Complex Systems (DOCS), international Conference on

作者： Caitong Yue Wenhao Wu Jing Liang Kunjie Yu Hongyu Lin Tuo Zhang School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Layout optimization (LO) is gaining increasing attention as competition among enterprises intensifies and the demand for cost reduction and efficiency improvement continues to grow. LO poses its peculiar challenges with Many-dimensions, Many-constraints and Many-optima. Although many approaches have been proposed for various LO applications, they focus only on the diversity and convergence of the objective space, neglecting the diversity of solutions, which is detrimental to solving potential issues. Therefore, this paper proposes a constrained multimodal multiobjective optimization (CMMO) framework based on dual-population multitask collaborative evolution. This framework assists in solving the workshop layout problem by creating an auxiliary task population and transferring knowledge to support the optimization process. Additionally, the dynamic narrowing of constraint boundaries and gradual expansion of the contrast neighborhood for the auxiliary tasks ensure a high degree of correlation with the main task, continuously providing supplementary evolutionary directions. Ultimately, compared to other constrained multimodal multiobjective algorithms and two classic genetic algorithms, the proposed algorithm proves to be more competitive in practical layout optimization problems.

关键词： Costs Heuristic algorithms Conferences Layout Evolutionary computation Search problems Mathematical models optimization Genetic algorithms Convergence

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Study on Bi-Objective Traffic Counting Location optimization Using Ant Colony Method

Study on Bi-Objective Traffic Counting Location Optimization...

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international workshop on Intelligent Systems and applications, ISA

作者： Yanping Du Sun Jian School of Machine-electricity Engineering Taiyuan University of Science and Technology Taiyuan China Department of Civil and Coastal Engineering University of Florida Gainesville FL USA

In order to solve the traffic counting location (TCL) problem, ant colony optimization (ACO) algorithm was introduced, and also some rudimentary numerical results for the TCL set covering model for a simple network were given out. Considering the two goals (maximal coverage and minimal resource utilization) of TCL problem are generally conflicting, this paper tries to incorporate some methods. Two intensification mechanisms are introduced when applying ACO into a similar location model with the same network location data. Then by comparing the optimal results and optimal percentage get, a clearly understanding about applying ACO algorithm into multi- objective location problems is achieved. Through a Pareto ranking selection strategy-Pareto-ranking, we can get the evolution of non-dominated solutions for the Sioux Falls network with 24 nodes, 76 links, and 182 O-D pairs. Also the performance of these non-dominated solutions can be further investigated using the flow capturing analysis method.

关键词： Ant colony optimization Traffic control Telecommunication traffic Resource management Costs Performance analysis Sun Sea measurements Transportation Genetic algorithms

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The Model of Dam Displacement Based on Improved Ant Colony Algorithm-Neural Networks

The Model of Dam Displacement Based on Improved Ant Colony A...

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international workshop on Database Technology and applications

作者： Yu-feng Jiang Juan Wang College of Conservancy and Hydropower Engineering Hohai University HHU Nanjing China Department of instrument science and engineering South-East University Nanjing China

According to the problems of the nonlinearity and non norm on dam displacement prediction, the dam displacement mode based on improved ant colony algorithm neural networks was proposed. The binary ant colony algorithm has been brought into the optimization of weights in neural networks. So that the shortcomings of the ant algorithm using in the combinatorial optimization in continuous field have been overcome, while the embarrassment of BP algorithm being vulnerable into the local optimum have been avoided. Therefore, this improved ant colony algorithm neural networks can have both rapid global convergence ability of binary ant colony algorithms and extensive mapping ability of neural networks. The dam displacement model based on the new ant colony algorithm-neural networks is built by mixed programming, and it has been used for project application. The analysis result shows that this mode is feasible in nonlinear fitting with a high accuracy, and so provides a new method for dam displacement prediction.

关键词： Neural networks Ant colony optimization Artificial neural networks Predictive models Convergence Robustness Safety Databases Educational institutions Hydroelectric power generation

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Multi-objective optimization of allocations and locations of incineration facilities with Voronoi diagram and genetic algorithm: Case study of northwest bay area in Chiba prefecture

Multi-objective optimization of allocations and locations of...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Taketo Kamikawa Takashi Hasuike School of Creative Science and Engineering Waseda Univ Shinjuku ward Japan

This research focuses on the two purposes of maximizing the amount of heat generated by incineration and minimizing the collection distance of waste, in determining allocations and locations of general waste incineration facilities as a case study of Chiba northwest bay area. For these purposes, we propose the multi-objective optimization with Voronoi diagram and genetic algorithm (MOVGA). As for the maximization of the amount of generated heat, we predict the amount by using regression equation of multiple linear regression analysis and formulate it as the set partitioning problem (SPP) to maximize the prediction value. As for the minimization of waste collection distances, we formulate it as the multi-Weber problem. To solve these two problems, we use MOVGA, which has the seeds of the Voronoi diagram as a gene. As a result of the survey using data of 2015 year of Chiba northwest bay area, in the case of 3 facilities it was found that the calorific value increased enough to cover the power of 4,205 households (converted to housing complex) per year despite the increase of 3% t-km per year.

关键词： Incineration Resource management Sociology Statistics Genetic algorithms optimization

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Model Selection of Least Squares Support Vector Regression Using Quantum-Behaved Particle Swarm optimization Algorithm

Model Selection of Least Squares Support Vector Regression U...

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international workshop on Intelligent Systems and applications, ISA

作者： Xiang-Li Li Lin-Cheng Zhou Chun-Bo Liu JiangSu College of Information Technology Wuxi Jiangsu China Wuxi Machinery and Electron Higher Professional and Technical School Wuxi Jiangsu China School of Communications and Control Engineering Jiangnan University Wuxi Jiangsu China

The selection for hyper-parameters is difficult and important to the performance of least squares support vector machines (LS-SVM). The existed parameters selection algorithms, such as the analytical, algebraic techniques and particle swarm optimization (PSO) algorithm, have their own shortcomings. In this paper, the problem of model selection for LS-SVM is discussed and a new method selecting the LS-SVM hyper-parameters is proposed based on the principles of the quantum-behaved particle swarm optimization (QPSO). The feasibility of this method is evaluated on data sets produced by sine function. Experimental results show that LS-SVM of QPSO-based hyper- parameters selection obtains better generalization capability and has more fast convergence speed than PSO-based hyper-parameters selection. Furthermore, the proposed method was applied to establish a soft- sensor model for content of Bisphenol A (CBPA) in rearrangement productive process. The results of real data simulation also show that this method is effective.

关键词： Least squares methods Particle swarm optimization Support vector machines Kernel Algorithm design and analysis Risk management Equations Educational institutions Information technology Machinery

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Optimized parallel sets for data intensive applications

Optimized parallel sets for data intensive applications

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international workshop on Database and Expert Systems applications

作者： K.-H. Eder L. Boszormenyi Institut für Informatik Universität Klagenfurt Klagenfurt Austria Alpen-Adria-Universitat Klagenfurt Klagenfurt KÃ¤rnten AT

An extension of a general-purpose programming language (gpPL) is presented. It enables parallelism, persistence and query optimization based on sets. The authors demonstrate that in gpPLs the primitive "set" can be generalised for the needs of database and expert system applications. Side-effect free declarative queries, based on set expressions, can be optimized and executed in parallel. Individual optimization and parallelization are integral parts of the language system and compiler. Very different combinations of persistent or volatile, and parallel or sequential, and optimized or non-optimized implementations are possible. This is eased by the fact that a great part of the implementation is located outside the compiler with the help of predefined interfaces. Different algebras, optimizers or algorithms can be considered. The same program can be executed without modification in various systems or platforms.

关键词： Optimizing compilers Computer languages Electronic mail Query processing Expert systems Algebra Relational databases Database systems Filters Data models

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Genetic Algorithm-Based optimization for Coding Metasurface Beam Control

Genetic Algorithm-Based Optimization for Coding Metasurface ...

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IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： Qi Xue Xiaojun Huang College of Communication and Information Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

In this paper, we introduce a 2-bit coded metasurface designed to control the deflection direction of the reflected beam by varying the size of the central octagon in the resonant layer. To validate this concept theoretically, we construct a $20 \times 20$ metasurface array and optimize its configuration using a genetic algorithm. This optimization allows us to manipulate vertically incident electromagnetic waves, achieving pitch deflections of 30° and 50° in planes with azimuth angles of 90° and 120°, respectively. The proposed coded metasurface demonstrates significant potential for applications in wireless communication and electromagnetic stealth.

关键词： Wireless communication Radio frequency Electric potential Azimuth Electromagnetic scattering Microwave communication Metasurfaces optimization Genetic algorithms Terahertz communications

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Dependability of on-line optimization techniques in real-time applications

Proceedings of the Workshop on Object-Oriented Real-Time Dep...

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Proceedings of the workshop on Object-Oriented Real-Time Dependable Systems (WORDS) 1999年 163-177页

作者： Hamidzadeh, Babak Shekhar, Shashi Univ of British Columbia Vancouver BC Canada

Real-time problem solvers require dependable, real-time search algorithms to meet task deadlines and to predict deadline violations. Presently it is difficult for existing real-time search algorithms to search and execute the solution by the deadline and to make deadline violation prediction. In this paper, we introduce a real-time search algorithm called Self-Adjusting Real-Time Search (SARTS). Given a timing constraint, SARTS adjusts itself based on the remaining time to deadline and allocates the planning time. As the timing constraints are relaxed, it will continue to improve its solutions progressively. The algorithm is able to predict deadline violations. Theoretical analyzes and experimental results reveal that, compared to the existing techniques, SARTS demonstrates a higher degree of predictability and a higher deadline compliance ability.

关键词： Real time systems

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Adaptive Genetic Algorithm Simulating Human Reproduction Mode and Its Application in Multi-Peak Function optimization

Adaptive Genetic Algorithm Simulating Human Reproduction Mod...

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international workshop on Intelligent Systems and applications, ISA

作者： Tai-shan Yan Dept. of Comput. Sci. Hunan Inst. of Sci. & Technol. Yueyang

Considering the limitation such as premature convergence and low global convergence speed of standard genetic algorithm, human reproduction mode is used for reference and an improved genetic algorithm named adaptive genetic algorithm simulating human reproduction mode (HRAGA) is proposed in this paper. The genetic operators of this algorithm include selection operator, help operator, adaptive crossover operator and adaptive mutation operator. The genetic individuals are separated into male individuals and female individuals, the age feature and consanguinity feature are fused into individuals. Two individuals with opposite sex can reproduce the next generation if they are distant consanguinity individuals and their age is allowable. The crossover operator and mutation operator are adjusted nonlinearly and adaptively. Experiments were taken on multi-peak function optimization. The validity and excellent performance of this algorithm was proved by the experimental results. Its global convergence speed and optimal solutions are all better than those of simple genetic algorithm.

关键词： Genetic algorithms Humans Animals Law Genetic mutations Convergence Computational modeling Computer simulation Evolution (biology) Application software

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