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IEEE Congress on Evolutionary Computation (CEC)

作者： Benkhelifa, Elhadj Moniri, Mansour Tiwari, Ashutosh de Rueda, Alfonso G. Staffordshire Univ Fac Comp Engn & Technol Stafford England Cranfield Univ Decis Engn Cent Mfg Dept Cranfield MK43 0AL Beds England

ISBN: (纸本)9781424478354

The design and optimisation of Energy Harvesting (EH) Micro-Electromechanical-Systems (MEMS) is of particular interest in this research. The application of such devices is becoming an attractive alternative to the traditional use of batteries in wireless and body sensor networks. An evolutionary multi-objective Design Optimisation (DO) Framework is developed to experiment with one class of EH-MEMS, namely, Piezoelectric, using a reconstructed analytical model of the system. The application of such a Framework in this application domain is unprecedented and has already shown very promising results and in some cases it outperformed the human engineer. A thorough analysis of the results has been undertaken, which reveals interesting conclusions about the behaviour and physics of such devices. Besides, the main features of the Framework are explored enabling the enhancement of the MEMS-DO.

关键词： multi-objective genetic algorithm design optimisation Energy Harvesting MEMS evolutionary computing Piezoelectric mobility

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multi-objective Shape Optimization in Generative Design: Art Deco Double Clip Brooch Jewelry Design 7th

Multi-Objective Shape Optimization in Generative Design: Art...

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iCatse International Conference on IT Convergence and Security (ICITCS)

作者： Sansri, Sunisa Kielarova, Somlak Wannarumon Naresuan Univ Fac Engn Dept Ind Engn Phitsanulok Thailand Naresuan Univ Fac Engn Ind Design Decis & Dev Res Unit iD3 Phitsanulok Thailand

ISBN: (纸本)9789811064517;9789811064500

This paper proposes multi-objective optimization generative design (MOOGD) system for generating shapes and optimizing two design objectives. The framework of this paper covers parametric modeling of the product shape configuration using Grasshopper plug-in in Rhinoceros software as well as multi objective optimization developed using Octopus plug-in on Grasshopper. This framework is applied onto the case study of Art Deco double clip brooch jewelry design. The main goals of the study are to design and to optimize shapes of the double clip brooch in two objectives. The first objective is to apply golden ratio to the generating shapes. The second one is to minimize the use of metal referring to weight of the brooch. In the system, MOOGD finally generates a Pareto front to show the optimal solutions, which artists or designers could further use in conceptual product design process. The illustration of the proposed system is provided in this paper.

关键词： Generative design multi-objective genetic algorithm multi objective optimization Pareto-optimal front Art deco double clip brooch

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Classifier Ensembles Optimization Guided by Population Oracle

Classifier Ensembles Optimization Guided by Population Oracl...

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IEEE Congress on Evolutionary Computation (CEC)

作者： dos Santos, Eulanda M. Sabourin, Robert Univ Fed Amazonas Manaus Amazonas Brazil Ecole Technol Super Montreal PQ Canada

ISBN: (纸本)9781424478354

Dynamic classifier ensemble selection is focused on selecting the most confident classifier ensemble to predict the class of a particular test pattern. The overproduce-and-choose strategy is a dynamic classifier ensemble selection method which is divided into optimization and dynamic selection phases. The first phase involves the test of different candidate ensembles in order to produce a population composed of the highest performing candidate ensembles. Then, the second phase calculates the domain of expertise of each candidate ensemble to pick up the solution with highest degree of certainty of its decision to classify the unknown test samples. It has been shown that the optimization phase decreases oracle, the upper bound of dynamic selection processes. In this paper we propose a hybrid algorithm to perform the optimization phase of overproduce-and-choose strategy. The proposed algorithm combines stochastic initialization of candidate ensembles of different sizes, with the traditional forward search greedy method. The objective is to apply oracle as search criterion during the optimization phase. We show experimentally that choosing the population of classifier ensembles taking into account the population oracle leads to increase the upper bound of the dynamic selection phase. Moreover, experimental results conducted to compare the proposed method to a multi-objective genetic algorithm (MOGA), demonstrate that our method outperforms MOGA on generating population of candidate ensembles with higher oracle rates.

关键词： Dynamic classifier ensemble selection hybrid search algorithm multi-objective genetic algorithm pattern recognition

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Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A multi-agent System Approach in Karaj Plain, Iran

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2024年第6期48卷 4751-4767页

作者： Khajeem Moghadam, Atena Saghafian, Bahram Najarchi, Mohsen Delavar, Majid Department of Civil Engineering Islamic Azad University Arak Branch Arak Iran Civil Department Science and Research Branch Islamic Azad University Tehran Iran Department of Water Resources Engineering Tarbiat Modares University Tehran Iran

The ever-increasing water demand has not only disrupted the demand-supply balance, causing conflicts among multiple stakeholders, but has also led to environmental impacts due to aquifer depletion. Today, agent-based modeling (ABM) is recommended for studying the effects of stakeholders’ social aspects on water resources, particularly groundwater resources. Aim of this study proposes a model for optimizing stakeholder behavior, which is coupled with numerical groundwater resource simulation and water resource optimization models in Karaj Plain, Iran. The simulation results of the aquifer showed that continuing the current situation will cause the aquifer level to drop by about 5 m. However, implementing the water resource optimization model, with goals of reducing water shortages and improving underground water conditions, will increase the aquifer reserve. This model will also provide 99.7% of the region’s drinking water at a rate of 3.03 MCM per year, raising the aquifer level by 3 m. Additionally, the optimization model of beneficiaries’ behavior and the analysis of proposed scenarios demonstrated that reducing agricultural water rights, combined with policies aimed at changing agents’ behavior in the agent base model, would increase the average groundwater level across the entire aquifer by about 5.5 m. Taking into account the local knowledge of the main water consumers, it is recommended to use multi-agent system models to incorporate the ideas of other agents (decision-makers) and develop the proposed cultivation pattern for effectively managing water resources and reducing environmental risks in arid and semi-arid climates. © The Author(s), under exclusive licence to Shiraz University 2024.

关键词： Agent-based model Environment Groundwater multi-objective genetic algorithm Optimal water allocation Simulation

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Real-time online Compensation the Nonlinearities and Uncertainties in Missile Servo Control System 27

Real-time online Compensation the Nonlinearities and Uncerta...

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27th Chinese Control and Decision Conference (CCDC)

作者： Feng, Zhang Kun Wei, Rao Da Bo, Zhu Jian Hua, Duan Li Luoyang Optoelect Technol Dev Ctr Luoyang 471009 Peoples R China

ISBN: (纸本)9781479970162

To restrain the influences of the nonlinearities and uncertainties in missile servo control systems, the active disturbance rejection control (ADRC) technique is introduced to real-time online estimate and compensate these factors. As to the problem of the difficulties in tuning ADRC parameters, an improved multi-objective genetic algorithm is adopted to solve the complete set of Pareto solutions for the control parameters, then a multi-criteria decision making algorithm based on utility function is chosen to determine the fittest solution. Then a test platform using TMS320C28346 floating-point DSP as control core is built to verify the research results. The experimental results show that the optimal ADRC parameter is appropriate for the servo control system. The system tracks the input commands smoothly and reliably, and has good robustness and adaptability to nonlinearities and parameters drifts. Also the strategy for tuning the ADRC parameters is practical and feasible.

关键词： Nonlinearity and Uncertainty Active Disturbance Rejection Control multi-objective genetic algorithm

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A New Bi-objective Location-routing Problem for Distribution of Perishable Products: Evolutionary Computation Approach

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Journal of Mathematical Modelling and algorithms in Operations Research 2015年第3期14卷 287-312页

作者： Khalili-Damghani, Kaveh Abtahi, Amir-Reza Ghasemi, Amir Department of Industrial Engineering South-Tehran Branch Islamic Azad University Tehran Iran Department of Knowledge Engineering and Decision Sciences Faculty of Management Economics University of Economic Sciences Tehran Iran

Supply, manufacture, and distribution of perishable products are challenging jobs in supply chains. Location of warehouses and routing of vehicles are essential issues to distribute perishable products properly. In this paper, a new bi-objective mixed integer mathematical programming is proposed to reduce the total cost of the supply chain and to balance the workload of distribution centers while the due dates of delivery of perishable product are met, concurrently. The considered properties and constraints of proposed model made it well-posed to illustrate the real life situation. As the proposed model is NP-Hard, an evolutionary algorithm called Non-Dominated Sorting genetic algorithm-II (NSGA-II) is customized to solve the problem. The structure of chromosome and genetic operators are customized for the problem. The performance of proposed NSGA-II and an efficient exact multi-objective method, called ε-constraint, is compared using accuracy and diversity metrics on several benchmark instances. © 2015, Springer Science+Business Media Dordrecht.

关键词： Bi-objective optimization Distribution of perishable products Location-routing problem Mixed-integer programming multi-objective genetic algorithm ε-constraint Method

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multi-objective Optimization of Underwater Towed System during CourseKeeping Motion

Multi-Objective Optimization of Underwater Towed System duri...

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International Conference on Mechanical Design and Simulation (MDS)

作者： Cheng, Yanyan Li, Yunzhou Yang, Chong Kunming Ship Borne Equipment Res & Test Ctr Res Off 1 Kunming Peoples R China

ISBN: (纸本)9781510655263;9781510655256

In this paper, the underwater towed system during the course-keeping motion was taken as the research object, in addition, the governing equation of motion was given as well. By the means of the experimental data obtained by the design of experiment, the second-order polynomial response surface models reflecting the steady-state motion characteristics of the towrope were established. Apart from mentioned above, the tail drag depth and the head tension were taken as objective functions, and the optimal solution set of Pareto was obtained by using the multi-objective optimization algorithm. The results shown that the approximate models were used to optimize the towrope when the parameters of the towrope were within a certain range, which can quickly and accurately analyzed the influences of each parameter on the steady-state motion characteristics of the towrope.

关键词： underwater towed system course-keeping motion optimal Latin hypercube design response surface method multi-objective genetic algorithm

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multi-classification of Alzheimer's Disease by NSGA-II Slices Optimization and Fusion Deep Learning 17th

Multi-classification of Alzheimer's Disease by NSGA-II Slice...

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17th Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE)

作者： Rojas-Valenzuela, Ignacio Rojas, Ignacio Delgado-Marquez, Elvira Valenzuela, Olga Univ Granada ETSIIT Granada Spain Univ Leon Dept Econ & Stat Leon Spain Univ Granada Fac Sci Granada Spain

ISBN: (纸本)9783031574290;9783031574306

This paper presents a two-phase hierarchical classifier for determining the different states in Alzheimer's disease (AD). In the first phase, an evolutionary system is developed to determine the most relevant slices (in both X-axis and Y-axis) of the magnetic resonance imaging (MRI) for the construction of a classifier. To obtain the image features, the biorthogonal wavelet transform 3.3 was used at level 2. Due to the high number of coefficients, a dimensionality reduction is performed using minimum Redundancy - Maximum Relevance algorithm (mRMR) and Principal Component Analysis (PCA). An evolutionary algorithm on a high-performance computer with GPU was used to optimize the slides. Support vector machine (SVM) was used in the fitness function to estimate the features of the classifier in a computationally simple way. In the second phase, using the different solutions of the Pareto front obtained by the evolutionary algorithm, a multiple deep learning system was developed, each of the systems having as input one of the selected slices of the analyzed solution. The solution with three slices (trade-off between complexity and accuracy) was used as the solution. The obtained hierarchical deep learning system fused the information from each system and analyzed the probabilities obtained for each class. As a final result, an accuracy of 92% was obtained for the six classes. A total of 1,200 patients from the Alzheimer's disease neuroimaging initiative (ADNI) database were used, corresponding to six different classes of patients (with varying degrees of dementia).

关键词： Alzheimer's Disease multiclass Classification multi-objective genetic algorithm Hierarchical System Deep Learning Ensemble System

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Determination of optimal location and capacity of detention facilities

Determination of optimal location and capacity of detention ...

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12th International Conference on Computing-and-Control-for-the-Water-Industry (CCWI)

作者： Lim, S. Ho, V. H. Lee, S. Y. Yoo, D. G. Kim, J. H. Korea Univ Coll Engn Sch Civil Environm & Architecture Engn Seoul 136713 South Korea

The importance of the effective distribution of runoff in sewer network throughout a detention facility has recently been recognized. This study aims to determine the location and the capacity of detention facilities in urban sewer network. The optimal location and capacity of the detention facilities can be determined by numerous iterations of an optimization model. In this paper, an interfacing Visual Basic (VB) programming tool with a SWMM (Storm Water Management Model) DLL for hydraulic and hydrological computation in sewer network completes the optimization routine. A multi-objective genetic algorithm (MOGA) is adopted to minimize both the total overflow and the installing cost. The developed model is applied to S city in South Korea. The goal in prevention performance of urban disaster is used as rainfall. The model will help engineers find effective alternatives to achieve the goal of the urban disaster prevention performance. (C) 2013 The Authors. Published by Elsevier Ltd.

关键词： optimal location and capacity detention facility goal in prevention performance of urban disaster multi-objective genetic algorithm

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A NSGA-II based Peak Load Shifting Optimization Method for Customers with Distributed Generators Considering Time-of-use Price 37

A NSGA-II based Peak Load Shifting Optimization Method for C...

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37th Chinese Control Conference (CCC)

作者： Zhou, Hao Ding, Min Hu, Bo Wang, Qingyi Tan, Zhili Li, Danyun China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China State Grid Energy Res Inst Co LTD Beijing 102200 Peoples R China Global Energy Interconnect Dev & Cooperat Org Beijing 100031 Peoples R China

ISBN: (纸本)9789881563958

With the extensive use of distributed generation, the traditional demand response analysis cannot meet the current requirements. This paper proposes a NSGA-II based peak load shifting optimization method for customers with distributed generators considering time-of-use price. Firstly, a fuzzy classification method divides the daily power into three time segments, peak hours, flat hours and valley hours. Secondly, on the basis of the time-of-use price, a peak load shifting optimization model is built with constraints and the objectives of minimizing the peak load, maximizing the valley load, and minimizing the peak-valley difference. Then, a NSGA-II based optimization method solves the optimal model and obtains the optimal electricity prices of different time segments to shift the peak load and the valley load. Finally, the simulation results shows the effectiveness of the proposed method.

关键词： Distributed Generation Demand Respond Time-of-use Price multi-objective genetic algorithm

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