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POLYNOMIAL NARX MODEL STRUCTURE OPTIMIZATION USING multi-objective genetic algorithm

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第10B期8卷 7341-7362页

作者： Loghmanian, Sayed Mohammad Reza Yusof, Rubiyah Khalid, Marzuki Ismail, Fatimah Sham Univ Teknol Malaysia Ctr Artificial Intelligence & Robot Kuala Lumpur Malaysia

Model structure selection is an important step in system identification which involves the selection of variables and terms of a model. The important issue is choosing a compact model representation where only significant terms are selected among all the possible ones beside good performance. This research explores the use of multi-objective optimization to minimize the complexity of a model structure and its predictive error simultaneously. The model structure representation is a polynomial non-linear autoregressive with exogenous input model. A new modified elitist non-dominated sorting genetic algorithm using clustered crowding distance (CCD) is proposed to find the exact model among non-dominated solutions, using some simulated examples which generate data set by mathematical equations. Simulation results demonstrated that the proposed algorithm can find the correct model with exact terms and values in all cases of problem. Furthermore, the effectiveness of the proposed algorithm is also studied by applying to the real process data sets, and the final model can be chosen from a set of non-dominated solutions referred as Pareto optimal front. The results show that the proposed clustered CD has better performance compared with the basic CD method.

关键词： NARX multi-objective genetic algorithm NSGA-II System identification Model structure selection Clustered crowding distance

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Local mean decomposition and artificial neural network approach to mitigate tool chatter and improve material removal rate in turning operation

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APPLIED SOFT COMPUTING 2020年 96卷 106714-106714页

作者： Gupta, Pankaj Singh, Bhagat Jaypee Univ Engn & Technol Guna MP India

Productivity has always been a major concern in the industry. It can be improved by increasing material removal rate. Regenerative chatter during machining is the major obstacle to attain this. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate (MRR). Chatter signals generated during the turning of Al 6061 have been acquired using a microphone. Initially, acquired signals have been processed using local mean decomposition (LMD) signal processing technique. The decomposed signals have been analyzed using different statistical chatter indicators considering Nakagami distribution approach for ascertaining the thresholds of chatter severity. Prediction models of most effective statistical chatter indicator and MRR have been developed using an artificial neural network (ANN). Moreover, this prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher MRR. Finally, obtained stable range has been validated by performing more experiments. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Tool chatter Local mean decomposition Nakagami distribution Artificial neural network multi-objective genetic algorithm

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Optimal design of classroom spaces in naturally-ventilated buildings to maximize occupant satisfaction with human bioeffluents/body odor levels

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BUILDING AND ENVIRONMENT 2020年 169卷 106543-000页

作者： Acosta-Acosta, Dario F. El-Rayes, Khaled Univ Illinois Dept Civil & Environm Engn 3112 Newmark Civil Engn Bldg205 N Mathews Urbana IL 61801 USA Univ Panamer Fac Ingn Alvaro Del Portillo 49 Zapopan 45010 Jalisco Mexico

Inadequate indoor air quality in education buildings has been reported to cause health problems, contagion, poor academic performance and absenteeism of occupants. To minimize these adverse effects, the use of natural ventilation systems and hybrid ventilation systems in buildings have increased in recent years. This paper presents the development of a novel optimization model that provides the capability of optimizing education building design in order to maximize occupant satisfaction in the classrooms space in terms of perception of human bioeffluents/body odor while minimizing the construction cost. The multi-objective optimization model is developed in three main stages: (1) model formulation stage that identifies relevant design variables, objective functions and constraints;(2) model implementation stage that executes the optimization computations;and (3) model evaluation stage that analyses the performance of the developed model using an application example. The findings of this performance analysis illustrates the capability of the developed optimization model to generate a wide range of optimal trade-offs solutions, where each provides a unique and optimal trade-off between occupant satisfaction with human bioeffluents/body odor levels and its construction cost. This enables designers to generate and analyze these optimal trade-offs and accordingly identify an optimal set of design decisions that maximize satisfaction with human bioeffluents/body odor levels of occupants while complying with specified budget constraint.

关键词： Natural ventilation Indoor air quality Human bioeffluents Human body odor Education buildings multi-objective genetic algorithm

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Thermo-economic optimization of gasification process with coal water slurry preheating technology

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ENERGY 2020年 199卷 117354-117354页

作者： Xiao, Juan Wang, Simin Ye, Shupei Dong, Jiayu Wen, Jian Zhang, Zaoxiao Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China

The energy efficiency of coal water slurry (CWS) gasification is less than that of pulverized coal gasification, a preheating technology using shell-and-tube heat exchangers (STHXs) with ladder-type fold baffles is introduced to improve energy efficiency of CWS gasification. The effect of structural parameters (folding angle, folding ratio and relative height) on cold gas efficiency and total cost is discussed coupling system design and unit equipment optimization, and multi-objective genetic algorithm combining response surface method is used to accomplish thermo-economic optimization. The results show that oxygen consumption decreases by 7.6% and cold gas efficiency increases by 6.5% than that without preheating technology. The local sensitivity illustrates that folding angle and folding ratio has a negative effect on cold gas efficiency and total cost, while relative height positively relates to cold gas efficiency and total cost. Moreover, structural parameters are regarded as optimization variables, maximizing cold gas efficiency and minimizing total cost are optimization functions. The optimization results indicate that the saving cost is the highest when folding angle, folding ratio and relative height are 30 degrees, 0.6 and 0.562, respectively. And a Pareto with multi-group of structural parameters obtained provides for design selections of CWS preheaters. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Thermo-economic optimization Coal water slurry Heat exchangers Gasification Preheating technology multi-objective genetic algorithm

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A multiple-model based internal model control method for power control of small pressurized water reactors

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ENERGY 2020年 210卷 118527-118527页

作者： Wang, Pengfei Chen, Zhi Liao, Longtao Wan, Jiashuang Wu, Shifa Xi An Jiao Tong Univ Shaanxi Key Lab Adv Nucl Energy & Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Shaanxi Engn Res Ctr Adv Nucl Energy Xian 710049 Peoples R China Nucl Power Inst China Sci & Technol Reactor Syst Design Technol Lab Chengdu 610041 Peoples R China

Small pressurized water reactors (PWRs) usually have variable operating conditions and complex operating environments. It is significant to design a robust control system to ensure stable and satisfactory reactor power maneuvers under large uncertainties. This paper purposes a multiple-model based internal model control (IMC) method for power control of small PWRs. Six local transfer function models are first developed from a nonlinear reactor core model over the entire operating range. Then, six local power controllers are designed for a small PWR based on these local models using the IMC scheme. The IMC filter time constant is determined by the genetic algorithm based multi-objective optimization to keep good balance between the control performance and cost. The local controllers are connected by a probability-weighted multiple model approach to generate a global IMC system for the reactor. Dynamic simulation results of the small PWR under the IMC scheme as well as performance assessments of the IMC scheme against the PID control scheme during typical transient operations have shown good reference tracking performances and strong disturbance rejection capabilities of the multiple-model based IMC system, demonstrating the feasibility and effectiveness of the proposed method for the power control of small PWRs. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Small pressurized water reactor Reactor power control system Internal model control multiple model approach multi-objective genetic algorithm

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Space optimizing design of ecological constructions based on attribute hierarchy model

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ARABIAN JOURNAL OF GEOSCIENCES 2020年第19期13卷 1016-1016页

作者： Han, Xu Zhongyuan Univ Technol Sch Art & Design Zhengzhou 450000 Peoples R China

Aiming at the problem of low user satisfaction of traditional ecological building space optimization methods, this paper introduces attribute analytic hierarchy process (AHP) model to effectively improve the effect of ecological building space optimization. This paper constructs an index system for the optimal design of ecological building space, and divides it into target level, criterion level and space level. Using the attribute analytic hierarchy process (AHP) model to construct attribute judgment matrix and weight, the general evaluation model of ecological building space optimization design is obtained. In this paper, multi-objective genetic algorithm is used to collect the spatial information of ecological building and determine the specific value of each parameter of ecological building. It used octopus to optimize the calculation results of multiple fitness functions and realized the optimized design of ecological building space. The experimental results show that the proposed method can significantly improve the user satisfaction of ecological building space, and people's satisfaction of the three open spaces of the overhead fire fighting channel, the main building, and the arc wall enclosure space are 62.13 points, 43.93 points, and 52.55 points, respectively, which shows that the satisfaction of the sunken courtyard is the highest.

关键词： Analytic hierarchy Ecological construction space Spatial hierarchy Fitness function multi-objective genetic algorithm

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The Spectral Optimization of a Commercializable multi-Channel LED Panel With Circadian Impact

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IEEE ACCESS 2020年 8卷 136498-136511页

作者： Saw, Yi Jiau Kalavally, Vineetha Tan, Chee Pin Monash Univ Malaysia Dept Elect & Comp Syst Engn Sch Engn & Intelligent Lighting Lab Bandar Sunway 47500 Malaysia Monash Univ Malaysia Dept Mechatron Engn Sch Engn & Adv Engn Platform Bandar Sunway 47500 Malaysia

There exists a need for the design of light emitting diode (LED) luminaires which can deliver both visual and non-visual benefits of light to humans. In this work, we introduce an optimization approach based on spectral shaping for a minimalistic and practical design of a circadian-tunable multi-channel luminaire which also outputs white light with high quality and luminous efficacy of radiation (LER). The spectral optimization approach utilizes multi-objective genetic algorithm to maximize circadian tunability, light quality and LER while minimizing the number of channels. Solution sets are constrained using the non-visual quality metric, Melanopic Efficacy of Luminous Radiation (MELR) from the Melanopic Equivalent Daylight Illuminance (MEDI) approach and the more stringent visual quality metric TM-30 in addition to conventional Color Rendering Index (CRI). By matching theoretically optimized LED parameters to commercially available LED parameters for commercialization purposes, we establish the maximum MELR tunability that is achievable with 4 and 5 LED channels and the resulting trade-off in efficacy and light quality. Based on the results and analysis in this work, we detail a spectral optimization approach to propel the field of indoor lighting towards human-centric lighting.

关键词： Optimization Light emitting diodes Lighting Visualization genetic algorithms Color Commercialization Circadian lighting human-centric lighting indoor lighting LED luminaire design melanopic equivalent daylight illuminance multi-objective genetic algorithm spectral optimization

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A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

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SENSORS 2020年第5期20卷 1474-1474页

作者： Chui, Kwok Tai Lytras, Miltiadis D. Liu, Ryan Wen Open Univ Hong Kong Sch Sci & Technol Dept Technol Hong Kong Peoples R China Deree Coll Sch Business & Econ Amer Coll Greece Athens 15342 Greece Effat Univ Effat Coll Engn POB 34689 Jeddah Saudi Arabia Wuhan Univ Technol Sch Nav Hubei Key Lab Inland Shipping Technol Wuhan 430063 Peoples R China

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals.

关键词： at-risk driving deep support vector machine driver drowsiness driver stress multi-objective genetic algorithm multiple kernel learning

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Assessment of stable cutting zone in CNC turning based on empirical mode decomposition and genetic algorithm approach

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第20期232卷 3573-3594页

作者： Shrivastava, Yogesh Singh, Bhagat Jaypee Univ Engn & Technol Dept Mech Engn AB Rd Guna 473226 Madhya Pradesh India

Assessment of optimum stable cutting zone is the key requirement to maintain high productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for deviation from these features. Despite the immense work done within this domain, still many aspects related to regenerative chatter remains unexplored. Usually, the chatter signals recorded from sensors are contaminated by background noise. Analysis of these contaminated signals results in faulty information regarding the identification of tool chatter. So, it becomes imperative that these signals should be denoised before further processing. In the present work, empirical mode decomposition technique has been adopted to pre-process the acquired raw chatter signals, which have been overlooked by the previous researchers. Initially, acoustic signals have been recorded by performing experiments at different combinations of cutting parameters. The preprocessed signals have been used to evaluate a new output parameter i.e. chatter index. Material removal rate has also been measured for each experiment. For estimating the dependence of output on input cutting parameters, mathematical models have been developed using response surface methodology. Moreover, the optimum cutting zone has been assessed by adopting multi-objective genetic algorithm. Finally, more experiments have been conducted to validate the obtained cutting zone. It has been found that the acquired cutting zone is capable of producing work pieces with good surface finish and acceptable material removal rate.

关键词： Tool chatter empirical mode decomposition intrinsic mode function response surface methodology multi-objective genetic algorithm

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Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories

Cloud-based Dynamic Distributed Optimisation of Integrated P...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Zhao, Shuai Dziurzanski, Piotr Przewozniczek, Michal Komarnicki, Marcin Indrusiak, Leandro Soares Univ York Dept Comp Sci York N Yorkshire England Wroclaw Univ Technol Dept Comp Intelligence Wroclaw Poland

ISBN: (纸本)9781450361118

In smart factories, process planning and scheduling need to be performed every time a new manufacturing order is received or a factory state change has been detected. A new plan and schedule need to be determined quickly to increase the responsiveness of the factory and enlarge its profit. Simultaneous optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation of process planning and scheduling is presented. It executes a multi-objective genetic algorithm on multiple subpopulations (islands). The number of islands is automatically decided based on the current optimisation state. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution.

关键词： Integrated process planning and scheduling multi-objective genetic algorithm Function as a Service Serverless Clouds

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