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Development of hybrid performance-based optimization algorithm for structures equipped with vibration damper devices

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ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING 2023年第2期23卷 123-123页

作者： Ayyash, Al-jbury Najad Amer Hejazi, Farzad Univ Putra Malaysia Dept Civil Engn Seri Kembangan Malaysia Univ West England Fac Environm & Technol Bristol England

Nowadays, various types of vibration damping systems are being implemented in different buildings to diminish seismic effects on structures. However, engineers are faced with the challenging task of developing an optimum design for structures utilizing a proper type of damping device based on new techniques such as the performance-based design method. Therefore, this research was aimed at developing a multi-objective optimization algorithm by hybridizing the particle swarm optimization (PSO) and gravitational search algorithm (GSA) to obtain an optimum design for structures equipped with vibration damper devices based on the performance-based design method. Then, the developed hybrid algorithm (PSOGSA) would be capable of optimizing the damping system simultaneously with the optimized details of the structural sections, including the steel rebars, by satisfying all the design criteria. For this purpose, a special process for the design of structures equipped with vibration damper devices according to the performance-based design method was developed by considering of a wide range of vibration damping systems. The proposed PSOGSA optimization framework was then implemented to design a 12-storey reinforced concrete structure equipped with different types of dampers to minimize the structural weight while satisfying all the prescribed performance-based design acceptance criteria. The results indicated that the proposed optimization method was able to successfully optimize the details of the structural members as well as the type and properties of the damper, which significantly improved the structural response in terms of the formation of plastic hinges and the structural movements.

关键词： hybrid optimization algorithm Particle swarm optimization Gravitational search algorithm Seismic damper devices Performance-based design

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A hybrid service selection optimization algorithm in internet of things

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2021年第1期2021卷 4-4页

作者： Zhang, Xiaofei Geng, Juncheng Ma, Jianwei Liu, Hao Niu, Shuangxia Mao, Wandeng State Grid Henan Elect Power Co Elect Power Res Inst Zhengzhou 450052 Peoples R China

With the scale of Internet of Things (IoT) continues to increase, it brings big challenges for service selection in a large-scale IoT. For solving this problem, a service selection method based on the enhanced genetic algorithm is proposed in this paper. To decrease the scale of service selection, this paper uses the lexicographic optimization approach and quality of service (QoS) constraint relaxation technique to find the candidate service with height QoS. Then, the IoT service selection problem is transformed into a single-objective optimization problem adopting a simple weighting method, and the final composite service meeting the user's QoS needs are obtained from the candidate service. The simulation results show that the proposed algorithm can efficiently and quickly achieve a composite service satisfying user's QoS needs, and is more suitable for solving the service composite problem in large-scale IoT services.

关键词： Internet of things hybrid optimization algorithm Service selection Quality of service

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Modeling and optimization of a stand-alone desalination plant powered by solar/wind energies based on back-up systems using a hybrid algorithm

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ENERGY 2022年第PartC期254卷

作者： Zhang, Weiping Maleki, Akbar Northwestern Polytech Univ Sch Software Xian Peoples R China Zhejiang Univ Binhai Ind Technol Res Inst Hangzhou Peoples R China Shahrood Univ Technol Fac Mech Engn Shahrood Iran

An autonomous reverse osmosis desalination plant powered by photovoltaic, wind turbines, diesel generators, and energy storage systems is considered as one of the effective ways to solve the difficulty of supplying potable water and electricity demands in remote areas. Nevertheless, modeling and achieving optimal size from an economic, environmental, and reliability point of view is necessary. In this study, four stand-alone hybrid desalination plants driven by renewable energy and back-up systems are designed and modeled to solve the difficulty of supplying potable water and electricity demands in remote areas. The hybrid structure consists of photovoltaic, wind turbines, diesel generators, and energy storage systems, which is optimized based on the minimum total cost and damage to the environment and suitable reliability. The optimization problem is solved by the hybrid optimization method based on two meta-heuristic methods. To ensure the superiority of the proposed optimization method, the obtained results are compared with two well-known algorithms. To verify the model, Davarzan Town, located in Iran, is selected as a case study. The effects of reliability indexes on optimal sizing of different hybrid systems are compared in detail. The results reveal that the suggested optimization method achieves the best system design. Also, it is seen that the stand-alone desalination plant powered by solar/ battery storage/diesel generator structure in different unreliability index is cheaper and cleaner than the other proposed systems.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Stand-alone hybrid renewable energy systems Reverse osmosis desalination Solar wind energy Back-up system hybrid optimization algorithm

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Novel Particle Swarm optimization algorithm Based on President Election: Applied to a Renewable hybrid Power System Controller

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第1期34卷 97-109页

作者： Yahyazadeh, M. Johari, M. S. HosseinNia, S. H. Vali E Asr Univ Rafsanjan Fac Engn Rafsanjan Iran Fac 3mE Dept Precis & Microsyst Engn Mekelweg 2 NL-2628 CD Delft Netherlands

Particle swarm optimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations have been accompanied with the nature of this algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president election is called President Election Particle Swarm optimization (PEPSO). This algorithm is trying to choose useful particles and omit functionless ones at initial steps of algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better reaction in worst conditions in finding parameters of the controller.

关键词： hybrid optimization algorithm Chaotic Function hybrid Power System Particle Swarm optimization algorithm President Election algorithm

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A Novel hybrid optimization Method of Shuffled Frog Leaping algorithm and Particle Swarm optimization

A Novel Hybrid Optimization Method of Shuffled Frog Leaping ...

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2013 2nd International Conference on Key Engineering Materials and Computer Science(KEMCS 2013)

作者： LIN Meijin LUO Fei XU Yuge LUO long Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Technology South China University of Technology Foshan university

Shuffled frog leaping algorithm(SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization(PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, w PSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

关键词： Shuffled frog leaping algorithm(SFLA) Particle swarm optimization(PSO) hybrid optimization algorithm Benchmark functions

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Certain investigation on optimization technique for sensor nodes in the bio medical recording system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 7145-7159页

作者： Ponsam, J. Godwin Nimala, K. Mohammad, Gousebaig Shitharth, S. Radha, Vijaya Kumar Reddy Rao, B. Srinivasa Srihari, K. Chandragandhi, S. SRM Inst Sci & Technol Dept Networking & Commun Kattankulathur India Vardhaman Coll Engn Hyderabad Comp Sci Dept Hyderabad India Kebri Dehar Univ Dept Comp Sci Kebri Dehar Ethiopia Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram AP India Lakireddy Bali Reddy Coll Engn Dept IT Mylavaram India SNS Coll Technol Dept Comp Sci & Engn Coimbatore India Karpagam Inst Technol AP AI & DS Coimbatore India

The creation of sensor-based software for health monitoring using Internet of Things (IoT) technology is the main goal of this project. The program's objective is to continuously monitor human physiological data, including ECG, SPO2, heart rate, and respiration, by employing biomedical sensor networks. These sensors collect data, which is then processed by a processor and transmitted to an edge server through a transceiver. A node of corner facilitates for real transmission has processed each data will be patient's phone and the clinicians' LED display. To address the optimization challenge, the program utilizes a Double Deep-Q-Network approach, with parameters optimized using a hybrid genetic algorithm-based simulated annealing technique. However, healthcare records obtained from the sensors are susceptible to change due to environmental factors, leading to potential performance issues. In order to overcome this challenge, an optimization approach is employed to refine the proposed technique, ensuring accurate prediction of readings. The study conducted experiments to evaluate the program's performance, utilizing various metrics and different parameters. The results to provide light on how well the program that was created for leveraging IoT technologies for health monitoring is working. This study presents an innovative sensor-based program for IoT technology-based health monitoring, which continuously monitors human physiological data. The program incorporates a hybrid optimization approach to ensure accurate prediction of readings, accounting for environmental factors. The proposed Double Deep-Q-Network and the evaluation metrics employed demonstrate the originality and contributions of this research in advancing health monitoring systems.

关键词： Biomedical record system double DQN bio-sensors edge computing hybrid optimization algorithm

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Collaborative optimization algorithm for learning path construction in E-learning

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COMPUTERS & ELECTRICAL ENGINEERING 2019年 77卷 325-338页

作者： Vanitha, V. Krishnan, P. Elakkiya, R. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai 600062 Tamil Nadu India ICAR Natl Acad Agr Res Management Hyderabad 500030 India SASTRA Deemed Univ Sch Comp Thanjavur 613401 India

In e-learning, learning object sequencing is a challenging task. It is difficult to sequence learning objects manually due to their abundant availability and the numerous combinations possible. An adaptive e-learning system that offers a personalized learning path would enhance the academic performance of learners. The main challenge in providing a personalized learning path is finding the right match between individual characteristics and learning content sequences. This paper presents a collaborative optimization algorithm, combining ant colony optimization and a genetic algorithm to provide learners with a personalized learning path. The proposed algorithm utilizes the stochastic nature of ant colony optimization and exploration characteristics of the genetic algorithm to build an optimal solution. Performance of the proposed algorithm has been assessed by conducting qualitative and quantitative experiments. This study establishes that the hybrid approach provides a better solution than the traditional approach. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Genetic algorithm ACO hybrid optimization algorithm Learning path Learning object sequence E-learning

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Propulsion performance diagnostics and optimization based on the estimation of measurement biases and hybrid optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2011年第G7期225卷 821-830页

作者： Zhang, J. Tang, H. L. Shen, G. Z. Yang, L. Y. Beijing Univ Aeronaut & Astronaut Natl Key Lab Sci & Technol Holist Control Beijing 100191 Peoples R China Beijing Univ Aeronaut & Astronaut Sch Jet Prop Beijing 100191 Peoples R China

This article deals with performance diagnostics and optimization of aircraft propulsion systems. To eliminate the adverse effects of unknown measurement biases on performance diagnostics, a new design approach based on measurement biases estimation is proposed. In this approach, measurement biases and the component deviation parameters (CDPs) are treated as a part of the augmented state vector, and multiple Kalman filters are designed based on different biases hypotheses to estimate the augmented states. Through the estimated measurement biases, the effects of actual biases are eliminated and accurate performance diagnostics is achieved. Moreover, considering the synthesis influence of linearization errors and initial values on the conventional linear programming (LP) method, a hybrid optimization algorithm based on LP and sequential quadratic programming (SQP) is investigated to improve the optimization accuracy and convergence speed. This algorithm uses a parallel subregion LP method to obtain the global feasible initial value, and then utilizes the SQP algorithm to search for the optimal solution. Simulations are carried out at cruise-operating conditions. The results demonstrate that the CDPs are estimated accurately, and the propulsion performance and online optimization ability are also improved.

关键词： propulsion system performance diagnostics and optimization measurement biases estimation hybrid optimization algorithm

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A new hybrid optimization ensemble learning approach for carbon price forecasting

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APPLIED MATHEMATICAL MODELLING 2021年 97卷 182-205页

作者： Sun, Shaolong Jin, Feng Li, Hongtao Li, Yongwu Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China

Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Carbon price forecasting Comprehensive evaluation index Model matching strategy hybrid optimization algorithm

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Double-Loop-optimization-Based Joint Parameter Tuning for Dynamic Matrix Control With Validation in Intelligent Cigarette Drying Process

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第5期20卷 7303-7315页

作者： Zhang, Yang Wang, Peng Li, Ning Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Dynamic matrix control (DMC) is widely used in intelligent manufacturing. Its performance is heavily affected by coupled parameters, e.g., horizons and weighting matrices. However, existing methods only focus on tuning of single type of parameter and cannot jointly tune them to improve the performance of DMC comprehensively. To bridge the gap, we propose a framework to tune DMC parameters based on double-loop-optimization jointly. The horizons of prediction and control are tuned in the outer loop, while the weighting matrices of error and control are tuned in the inner loop. An improved genetic algorithm fused with the particle swarm optimization is developed to minimize the cost function considering the constraints on horizons and weighting matrices. In the developed algorithm, population evolution and individual updating are deeply integrated to improve global optimality. The efficacy of the proposed framework is verified with real data of moisture control in cigarette drying process, which improves the quality of the moisture control by stabilizing the production equipment and reducing energy consumption.

关键词： Monte Carlo methods Tuning Moisture control Statistics Sociology Predictive models Heuristic algorithms Double-loop-optimization (DLO) framework dynamic matrix control (DMC) hybrid optimization algorithm intelligent moisture control joint tuning

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