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multi-objective particle swarm optimization on ultra-thin silicon solar cells

JOURNAL OF OPTICS-INDIA 2020年第4期49卷 446-454页

作者： Atalay, Ipek Anil Gunes, Hasan Alper Alpkilic, Ahmet Mesut Kurt, Hamza TOBB Univ Econ & Technol Dept Elect & Elect Engn TR-06560 Ankara Turkey

Finding optimized parameters for any photonic device is a challenging problem, because as the search space enlarges the computation time and design complexity increase. For higher performance solar cells, various studies have been carried out to procure optimized parameters, to attain better performance and low cost as well. In this study, we used a multi-objective particle swarm optimization approach to search design space effectively and obtain fixed parameters for enhanced solar spectrum absorption. Numerical investigations are conducted for pyramid surface pattern, to find proper solar cell parameters for minimum reflection and maximum light trapping which give rise to enhanced absorption of photons. For the ultra-thin-film silicon solar cell having a thickness of 1 mu m, a designed double-sided pyramid structure provides an ideal short-circuit photocurrent of 34.23 mA/cm(2). In this regard, the proposed approach can be applied to different film thicknesses of semiconductors for different photonic applications by manipulating the reflection/transmission coefficient and light trapping mechanism.

关键词： Solar cells Anti-reflection Absorption enhancement Surface texturing Light trapping multi-objective particle swarm optimization

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multi-objective particle swarm optimization with reverse multi-leaders

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第7期20卷 11732-11762页

作者： Chen, Fei Liu, Yanmin Yang, Jie Yang, Meilan Zhang, Qian Liu, Jun Guizhou Univ Sch Math & Stat Guiyang 550025 Peoples R China Zunyi Normal Coll Sch Math Zunyi 563002 Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China

Despite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution to the aforementioned issue. First, the convergence strategy of global ranking and the diversity strategy of mean angular distance are proposed, which are used to update the convergence archive and the diversity archive, respectively, to improve the convergence and diversity of solutions in the archives. Second, a reverse selection method is proposed to select two global leaders for the particles in the population. This is conducive to selecting appropriate learning samples for each particle and leading the particles to quickly fly to the true Pareto front. Third, an information fusion strategy is proposed to update the personal best, to improve convergence of the algorithm. At the same time, in order to achieve a better balance between convergence and diversity, a new particle velocity updating method is proposed. With this, two global leaders cooperate to guide the flight of particles in the population, which is conducive to promoting the exchange of social information. Finally, RMMOPSO is simulated with several state-of-the-art MOPSOs and multi-objective evolutionary algorithms (MOEAs) on 22 benchmark problems. The experimental results show that RMMOPSO has better comprehensive performance.

关键词： multi-objective particle swarm optimization global ranking mean angular distance reverse selection information fusion

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multi-objective particle swarm optimization of industrial natural gas dehydration process

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CHEMICAL PAPERS 2023年第2期77卷 1067-1080页

作者： Al-Jammali, Ali Sameer Ismail Amooey, Ali Akbar Nabavi, Seyed Reza Univ Mazandaran Fac Technol & Engn Dept Chem Engn Babolsar Iran Univ Mazandaran Fac Chem Dept Appl Chem Babolsar Iran

Dehydration by TEG is widely used in the NGD process to prevent corrosion and plugging of the equipment, valves, and flow lines. In this process, TEG is usually lost in the system due to vaporization and carryover. Therefore, it is necessary to optimize the dehydration process to achieve the allowable water content in the dry gas, and also to minimize heat duty, and TEG losses simultaneously. Therefore, the problem has multi-objective for optimization. The process is simulated by using Aspen Hysys, and the thermodynamic model was glycol-package. Validation of the simulation results was done by comparing with the plant data. Several bi- and tri-objective optimization problems are solved;these problems involved minimization of water content, heat duty, TEG loss and maximization of dry gas flow rate. Wet gas temperature, flow rate, pressure, TEG temperature and mass flow rate, and rich glycol temperature were decision variables. MOPSO algorithm was used for obtaining the Pareto fronts. MOO of the NDG provides a range of optimal operating conditions and objective values;a suitable operating point can be selected based on the higher knowledge like profit.

关键词： Natural gas Dehydration process Tri-ethylene glycol multi-objective particle swarm optimization Aspen Hysys

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multi-objective particle swarm optimization with R2 indicator and adaptive method

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COMPLEX & INTELLIGENT SYSTEMS 2021年第5期7卷 2697-2710页

作者： Gu, Qinghua Jiang, Mengke Jiang, Song Chen, Lu Xian Univ Architecture & Technol Sch Management Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & D Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China

multi-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective optimization problems. This is mainly because of the imbalance between convergence and diversity that occurs when increasing the selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution and adaptive method is developed to improve the performance of MOPSO. First, a new global best solutions selection mechanism with R2 contribution is introduced to select leaders with better diversity and convergence. Second, to obtain a uniform distribution of particles, an adaptive method is used to guide the flight of particles. Third, a re-initialization strategy is proposed to prevent particles from trapping into local optima. Empirical studies on a large number (64 in total) of problem instances have demonstrated that ANMPSO performs well in terms of inverted generational distance and hyper-volume metrics. Experimental studies on the practical application have also revealed that ANMPSO could effectively solve problems in the real world.

关键词： Many-objective optimization problems multi-objective particle swarm optimization R2 contribution Adaptive strategy

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multi-objective particle swarm optimization Algorithm Based on Self-Update strategy

Multi-objective Particle Swarm Optimization Algorithm Based ...

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International Conference on Industrial Control and Electronics Engineering (ICICEE)

作者： Wang Jianguo Liu Wenjing Zhang Wenxing Yang Bin Inner Mongolia Univ Sci & Technol Sch Mech Engn Baotou Peoples R China

ISBN: (纸本)9780769547923

In multi-objective particle swarm optimization (MOPSO) algorithms, improving the diversity of solutions is very difficult yet an important problem. In this paper, a new MOPSO algorithm of searching the Pareto-optimal solution is introduced, called multi-objective particle swarm optimization algorithm based on self-update strategy (SU-MOPSO). The mainly strategy of SU-MOPSO is that improving the diversity of each particle local best position (usually called pbest) to satisfy the swarm update's needs, and fundamentally enhances the diversity of Pareto set by rising the candidate quantity. The proposed SU-MOPSO algorithm has been compared with ES-MOPSO algorithm. The results demonstrate that the SU-MOPSO algorithm has gained better convergence with even distributing and diversity of Pareto set.

关键词： multi-objective particle swarm optimization Diversity of solutions Pareto-optimal solution Self-update strategy

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multi-objective particle swarm optimization for Control Laws Design

Multi-Objective Particle Swarm Optimization for Control Laws...

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2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)

作者： Liu Xiaoxiong Xu Heng Wu Yan Li PengHui Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9783037857502

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

关键词： multi-objective particle swarm optimization Genetic Hybrid Attitude Hold control

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multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Zhang, Xiangyin Xia, Shuang Li, Xiuzhi Zhang, Tian Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China

In order to solve the multiple unmanned aerial vehicles (UAVs) collaborative path planning problem under complex environments with multiple constraints, the multi-objective particle swarm optimization algorithm with multi-mode collaboration based on reinforcement learning (MCMOPSO-RL) is proposed in this paper to find optimal paths and handle constraints simultaneously. Reinforcement learning (RL) is applied to enable the proposed algorithm to choose the suitable position updated mode to achieve the high performance. multi-mode collaboration strategy is developed to update the particle positions, where three modes are designed to balance the population diversity and the convergence speed, including the exploration, exploitation modes, and the hybrid update mode. Experimental results show that MCMOPSO-RL can solve the path planning problem for multiple UAVs more efficiently and robustly than other algorithms. (C) 2022 Published by Elsevier B.V.

关键词： Path planning multi-objective particle swarm optimization Unmanned air vehicle Reinforcement learning

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Adaptive candidate estimation-assisted multi-objective particle swarm optimization

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Science China(Technological Sciences) 2022年第8期65卷 1685-1699页

作者： HAN HongGui ZHANG LinLin HOU Ying QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

The selection of global best(Gbest) exerts a high influence on the searching performance of multi-objective particle swarm optimization algorithm(MOPSO). The candidates of MOPSO in external archive are always estimated to select Gbest. However,in most estimation methods, the candidates are considered as the Gbest in a fixed way, which is difficult to adapt to varying evolutionary requirements for balance between convergence and diversity of MOPSO. To deal with this problem, an adaptive candidate estimation-assisted MOPSO(ACE-MOPSO) is proposed in this paper. First, the evolutionary state information,including both the global dominance information and global distribution information of non-dominated solutions, is introduced to describe the evolutionary states to extract the evolutionary requirements. Second, an adaptive candidate estimation method,based on two evaluation distances, is developed to select the excellent leader for balancing convergence and diversity during the dynamic evolutionary process. Third, a leader mutation strategy, using the elite local search(ELS), is devised to select Gbest to improve the searching ability of ACE-MOPSO. Fourth, the convergence analysis is given to prove the theoretical validity of ACE-MOPSO. Finally, this proposed algorithm is compared with popular algorithms on twenty-four benchmark functions. The results demonstrate that ACE-MOPSO has advanced performance in both convergence and diversity.

关键词： multi-objective particle swarm optimization evolutionary state information adaptive candidate estimation convergence and diversity convergence analysis

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A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

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Acta Mechanica Sinica 2018年第2期34卷 275-284页

作者： Y.Zheng J.Chen Department of Mechanics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory for Engineering Structural Analysis and Safety Assessment Wuhan 430074China

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.

关键词： multi-objective particle swarm optimization Kriging meta-model Trapezoid index Deepwater composite riser

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A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm

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APPLIED MATHEMATICAL MODELLING 2019年 76卷 717-740页

作者： He, Zhaoshuang Chen, Yanhua Shang, Zhihao Li, Caihong Li, Lian Xu, Mingliang Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450000 Henan Peoples R China

Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Wind speed forecasting multi-objective particle swarm optimization Echo state network Moving window Combined model

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