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Relaxed static output feedback control for discrete-time Takagi-Sugeno fuzzy systems: A switching sequence convex optimization algorithm

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INFORMATION SCIENCES 2024年 678卷

作者： Gao, Jingjing Xie, Xiangpeng Xia, Jianwei Liaocheng Univ Sch Math Sci Liaocheng 252059 Shandong Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China

In this paper, a new switching sequence convex optimization (SSCO) algorithm is proposed for solving non-convex optimization problem with complex time-varying relaxation matrix structures that arises during output feedback design. Firstly, the introduced time-varying relaxation matrix combines the membership functions and the designed switching mechanism to adjust the positive and negative terms of the inequality constraints. As a result, relaxed controller design conditions with complex matrix structures are established. The proposed SSCO algorithm employs switching optimization variables and inner approximation strategy, which is able to compute non-convex optimization problems with complex matrix structures more flexibly and converge quickly. It is worth noting that the implementation of the SSCO algorithm requires a set of strictly feasible initial solutions. Therefore, an initialization iterative algorithm is proposed, which overcomes the difficulties of transforming the solving problem into a typical non-convex optimization problem and linearizing multiple different concave parts, by which a set of optimized feasible solutions are obtained. Finally, simulation examples are used to demonstrate the superiority of the design scheme proposed in this paper.

关键词： Takagi-Sugeno fuzzy systems Static output feedback Time-varying relaxation matrix technique optimization algorithm

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Parameters identification of photovoltaic models using modified Rao-1 optimization algorithm

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OPTIK 2021年 231卷 166439-166439页

作者： Jian, Xianzhong Zhu, Yizhuang Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

Accurate and robust parameter identification method is helpful to the imitation, control and optimization of photovoltaic (PV) system. Therefore, it is necessary to create some more accurate and robust algorithms. A new metaheuristic algorithm modified Rao-1(MRao-1) is proposed by combining Rao-1 with two-way updating strategy based on random individuals. In this strategy, the current or random individuals are chosen as updated starting point and the difference between random individuals is taken as updated direction. MRao-1 inherits the advantages of the original Rao-1 algorithm without additional special parameters and improves the global search ability of Rao-1 significantly without increasing the time complexity of Rao-1. MRao-1 is evaluated on benchmark functions and applied to parameter identification of PV models. The results show that the accuracy and robustness of MRao-1 algorithm are superior to the original algorithm and other recent excellent algorithms. Therefore, MRao-1 is a promising parameter identification algorithm.

关键词： Photovoltaic model Parameter identification Rao-1 algorithm optimization algorithm

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Comparison of the Prediction of Effective Moment of Inertia of FRP Rebar-Reinforced Concrete by an optimization algorithm

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MATERIALS 2023年第2期16卷 621-621页

作者： Jang, Nag-Seop Kim, Young-Hwan Oh, Hong-Seob Gyeongsang Natl Univ Dept Civil Engn Jinju 52725 South Korea

FRP (fiber-reinforced polymer)-reinforced concrete members have larger deflection than reinforced concrete members because of the low modulus of elasticity of the FRP bar. In this paper, we proposed a new effective moment of inertia equation to predict the deflection of FRP-reinforced concrete members based on the harmony search algorithm. The harmony search algorithm is used to optimize a function that minimizes the error between the deflection value of the experimental result and the deflection value expected from the specimen's specifications. In the experimental part, four GFRP (Glass Fiber-Reinforced Polymer)-and BFRP (Basalt Fiber-Reinforced Polymer)-reinforced concrete slab specimens were manufactured and tested. FRP-reinforced concrete slabs were reinforced with GFRP and BFRP rebars on spiral rib surfaces. The effects of the FRP reinforcement ratio and balanced reinforcement ratio (rho(f )/rho(fb)), the moment of inertia of the transformed cracked section and the gross moment of inertia (I-cr/I-g), and the cracking moment and the maximum service load moment (M-cr/M-a) on the effective moment of inertia have been considered. The experimental results and predicted results of the flexural testing of concrete slabs reinforced with FRP rebars were compared, and the experimental results were in good agreement with the calculated values using the proposed effective moment of inertia equation.

关键词： FRP rebars effective moment of inertia deflection optimization algorithm harmony search algorithms

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Development of Design Method for River Water Source Heat Pump System Using an optimization algorithm

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ENERGIES 2022年第11期15卷 4019页

作者： Kwon, Youngsik Bae, Sangmu Nam, Yujin Pusan Natl Univ Dept Architectural Engn 2 Busandaehak Ro 63 Busan 46241 South Korea

River water source heat pump (RWSHP) systems are being proposed to reduce the energy consumption and carbon emissions of buildings. The RWSHP system is actively applied to large-scale buildings due to its stable performance. The application of RWSHP in large-scale facilities requires an accurate capacity design with considerations of building load, heat source, and environment conditions. However, most RWSHP systems are over-designed based on peak load of buildings. These design methods, based on peak loads, are economically and environmentally disadvantageous. Therefore, this paper aims to development an optimal design method, both economically and environmentally, for the RWSHP system. To develop this optimal design method, a simulation model was created with an optimization algorithm. The economics of the RWSHP system were calculated bases on present worth of annuity factor. Moreover, CO2 emissions were estimated using the life cycle climate performance proposed by the International Institute of Refrigeration. The total cost of the proposed RWSHP system that apply the optimum design method decreased by 24% compared to conventional RWSHP systems. Moreover, CO2 emissions of the proposed RWSHP system reduced by 4% compared to conventional RWSHP systems.

关键词： river water source heat pump optimum design method optimization algorithm CO2 emissions economic analysis

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An improved phase field method by using statistical learning theory-based optimization algorithm for simulation of martensitic transformation in NiTi alloy

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COMPUTATIONAL MATERIALS SCIENCE 2020年 172卷 109292-000页

作者： Wei, C. Ke, C. B. Liang, S. B. Cao, S. Ma, H. T. Zhang, X. P. South China Univ Technol Sch Mat Sci & Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510640 Guangdong Peoples R China Guangzhou Univ Earthquake Engn Res & Test Ctr Guangzhou Guangdong Peoples R China

An improved phase field method by using statistical learning theory based optimization algorithm is developed for solving the phase field equations through building simple relationships between the key phase field variables and the phase evolution driving force, and using statistical analysis of mass computed data during phase field simulation. Phase field simulation results of growth of R phase and the B2-R phase transformation in a Ni-rich Ni50.5Ti49.5 alloy by using the proposed statistical strategy algorithm are compared with that using the conventional numerical algorithm, which demonstrates that with coupling the statistical learning theory, i.e., by means of the optimization algorithm, the credible simulated microstructure is obtained while maintaining high accuracy, and meanwhile the computational time has been significantly reduced.

关键词： Martensitic transformation Phase field modeling optimization algorithm Statistical learning theory

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Accident parameter prediction method for lead-bismuth cooled reactor based on a multivariate LSTM network coupled with an optimization algorithm

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ANNALS OF NUCLEAR ENERGY 2023年第1期193卷

作者： Chen, Zhifei Liu, Zijing Ji, Nan Yang, Junkang Wang, Kai Univ Liverpool Brownlow Hill Liverpool L6 1AJ England Univ South China Sch Nucl Sci & Technol Hengyang 421001 Hunan Peoples R China

Accurate acquisition of key parameters of lead-bismuth cooled reactors under accident conditions is a prerequisite for reactor safety analyses. In this work, four optimization algorithms(Particle Swarm algorithm, Genetic algorithm, Quantum genetic algorithm, Whale optimization algorithm) are used to improve the forecast efficiency of a long short-term memory (LSTM) neural network using hyperparameter optimization, and a method is developed to predict the parameters of a lead-bismuth cooled reactor (MARS-3) during a loss of flow accident. A comprehensive evaluation of the proposed method is performed using the TOPSIS technique based on the data samples generated using the sub-channel code SUBCHANFLOW. The results show that the prediction performance of the multivariate LSTM neural network coupled with the particle swarm optimization method is optimal, and its computational efficiency is 438 times that of SUBCHANFLOW. Overall, the study findings can help improve the prediction efficiency for key thermal parameters of lead-bismuth cooled reactors and enhance the emergency response capability of such reactors.

关键词： Multivariate LSTM optimization algorithm Lead-bismuth cooled reactor Accident parameter prediction SUBCHANFLOW

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Evaluation of an Electrical Equivalent Model Using Measurements and an optimization algorithm - Application to a Pyroelectric Sensor

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2015年第7期24卷 1550097-1550097页

作者： Sifi, Nejmeddine Touayar, Oualid Univ Carthage Natl Inst Appl Sci & Technol Res Lab Mat Measurements & Applicat INSATDept Genie Phys & InstrumentatCtr Urbain N BP 676 Tunis 1080 Tunisia

A technique to estimate the parameters of an equivalent electrical model of a pyroelectric sensor prototype, based on measurements and an optimization algorithm, is presented. The dynamic behavior predicted by the equivalent electrical model is compared with measurements, and the effect of some geometrical mismatches is investigated as well. The optimization algorithm is a judicious combination of two search methods: random (a uniform statistical distribution) and deterministic (a conjugate gradient method). The reliability of the optimization algorithm to estimate the parameters values of the equivalent electrical model is also studied and discussed. The sensor equivalent electrical model can be used as a quick and intuitive analysis tool to allow the simulation of the device in a system-level design environment.

关键词： Electrical equivalent model parameters estimation optimization algorithm pyroelectric sensor measurement modeling

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Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion optimization algorithm

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ENERGIES 2023年第20期16卷 7185页

作者： Wu, Yanmin Liu, Jiaqi Wang, Lu An, Yanjun Zhang, Xiaofeng Naval Univ Engn Coll Elect Engn Wuhan 430033 Peoples R China Zhengzhou Univ Light Ind Coll Bldg Environm Engn Zhengzhou 450002 Peoples R China

Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved;the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, mor

关键词： distribution network reconfiguration chaos optimization CPSCSFO algorithm optimization algorithm distributed generation

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Boosted sooty tern optimization algorithm for global optimization and feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Houssein, Essam H. Oliva, Diego Celik, Emre Emam, Marwa M. Ghoniem, Rania M. Minia Univ Fac Comp & Informat Al Minya Egypt Univ Guadalajara CUCEI Dept Innovac Basada Informat & Conocimiento Guadalajara Jalisco Mexico Duzce Univ Dept Elect & Elect Engn Fac Engn Duzce Turkiye Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Mansoura Univ Dept Comp Mansoura Egypt

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classifica-tion accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern optimization algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization.

关键词： Sooty Tern optimization algorithm (STOA) Feature selection Dimensionality reduction Metaheuristics optimization algorithm

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A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

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PEERJ COMPUTER SCIENCE 2022年 8卷 e910页

作者： Dehghani, Mohammad Hubalovsky, Stepan Trojovsky, Pavel Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove Czech Republic Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove Czech Republic

In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO's performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO's exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.

关键词： optimization optimization algorithm optimization problem algorithm of best and worst members of the population

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