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Shape optimization method of grid shell structures based on PGSA

JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2025年 227卷

作者： Shi, Kairong Lin, Jinlong Jiang, Zhengrong Chen, Runyang South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510641 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg & Urban Sci Guangzhou 510641 Peoples R China

Aiming at the structural shape optimization of grid shell structures, PGSA (plant growth simulation algorithm) is firstly introduced to the field of structural shape optimization. Based on the basic principle of PGSA, a novel shape optimization method of grid shell structures based on improved GSL&PS-PGSA (growth space limitation & parallel search) is proposed. The corresponding structural optimization process is given, and the optimization programs are compiled by MATLAB and the APDL (ANSYS Parametric Design Language). The shape optimization examples of typical grid shell structures are analyzed by the proposed method. The results show that the proposed shape optimization method based on improved GSL&PS-PGSA can effectively explore rational structure form, improve mechanical properties of the structure, and reduce structure sensitivity to geometric defects. As a result, it provides a new approach for shape optimization of grid shell structures.

关键词： plant growth simulation algorithm Grid shell structure Shape optimization

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Improved plant growth simulation and genetic hybrid algorithm (PGSA-GA) and its structural optimization

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ENGINEERING COMPUTATIONS 2018年第1期35卷 268-286页

作者： Shi, Kairong Ruan, Zhijian Jiang, Zhengrong Lin, Quanpan Wang, Long South China Univ Technol State Key Lab Subtrop Bldg Sci Guangzhou Guangdong Peoples R China

Purpose The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and genetic hybrid algorithm (PGSA-GA), for solving structural optimization problems. Design/methodology/approach PGSA-GA is based on PGSA and three improved strategies, namely, elitist strategy of morphactin concentration calculation, strategy of intelligent variable step size and strategy of initial growth point selection based on GA. After a detailed formulation and explanation of its implementation, PGSA-GA is verified using the examples of typical truss and single-layer lattice shell. Findings Improved PGSA-GA was implemented and optimization was carried out for two typical optimization problems;then, a comparison was made between the PGSA-GA and other methods. The results show that the method proposed in the paper has the advantages of high efficiency and rapid convergence, which enable it to be used for the optimization of various types of steel structures. Originality/value Through the examples of typical truss and single-layer lattice shell, it shows that the optimization efficiency and effect of PGSA-GA are better than those of other algorithms and methods, such as GA, secondary optimization method, etc. The results show that PGSA-GA is quite suitable for structural optimization.

关键词： plant growth simulation algorithm Elitist strategy Hybrid algorithm Intelligent variable step size Structural optimization

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A novel PGSA-PSO hybrid algorithm for structural optimization

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ENGINEERING COMPUTATIONS 2020年第1期37卷 144-160页

作者： Jiang, Zhengrong Lin, Quanpan Shi, Kairong Pan, Wenzhi South China Univ Technol Sch Civil Engn & Transportat Guangzhou Peoples R China Architectural Design & Res Inst Guangdong Prov Guangzhou Peoples R China

Purpose The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and particle swarm optimization hybrid algorithm (PGSA-PSO hybrid algorithm), for solving structural optimization problems. Design/methodology/approach To further enhance the optimization efficiency and precision of this algorithm, the optimization solution process of PGSA-PSO comprises two steps. First, an excellent initial growth point is selected by PSO. Then, the global optimal solution can be obtained quickly by PGSA and its improved strategy called growth space adjustment strategy. A typical mathematical example is provided to verify the capacity of the new hybrid algorithm to effectively improve the global search capability and search efficiency of PGSA. Moreover, PGSA-PSO is applied to the optimization design of a suspended dome structure. Findings Through typical mathematical example, the improved strategy can improve the optimization efficiency of PGSA considerably, and an initial growth point that falls near the global optimal solution can be obtained. Through the optimization of the pre-stress of a suspended dome structure, compared with other methods, the hybrid algorithm is effective and feasible in structural optimization. Originality/value Through the examples of suspended dome structure, it shows that the optimization efficiency and precision of PGSA-PSO are better than those of other algorithms and methods. PGSA-PSO is effective and feasible in structural optimization problems such as pre-stress optimization, size optimization, shape optimization and even topology optimization.

关键词： plant growth simulation algorithm Particle swarm optimization Hybrid algorithm Structural optimization Suspended dome structure

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Cross-efficiency intervals integrated ranking approach based on the generalized Fermat-Torricelli point

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷

作者： Wang, Yuhong Wu, Dongdong Qian, Wuyong Li, Hui Jiangnan Univ Sch Business Wuxi 214122 Jiangsu Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

As a significant extension of data envelopment analysis studies, cross-efficiency has been commenly adopted to rank the performances of decision-making units (DMUs). Interval cross-efficiency techniques can solve the nonuniqueness problem by considering all possible weight sets in weight space. Most existing cross-efficiency approaches employ the average cross-efficiency to aggregate the cross-efficiency matrix (CEM), but the consensus preferences among DMUs acquire little consideration. In this paper, we develop a new integrated ranking technique for cross-efficiency intervals. Cross-efficiency methods with crisp and interval input-output data are used to construct generalized interval CEMs. The cross-efficiency intervals are projected into twodimensional coordinates, and the optimal rally point is generated using the plant growth simulation algorithm to solve the generalized Fermat-Torricelli problem. A possibility distribution function is applied to transform the aggregated interval CEMs, and then we obtain the ultimate cross-efficiency rankings of all DMUs. Two illustrations are provided to demonstrate the validity of the proposed approach.

关键词： Data envelopment analysis Cross-efficiency aggregation Group decision making Consensus preference plant growth simulation algorithm Possibility distribution function

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A New Method for Comprehensive Evaluation of Air Quality in Urban Agglomeration

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Atmospheric and Climate Sciences 2021年第3期11卷 547-562页

作者： Li Lei Xinli Chen Wei Liu Business School Jiangnan University Wuxi China School of Social Development and Public Policy Fudan University Shanghai China School of Internet of Things Engineering Jiangnan University Wuxi China

In this study, a new method for a comprehensive evaluation of air quality in urban agglomerations was developed based on a prototype used to solve the spatial Steiner-Weber point. With this method, the air quality information of each city in the city group is aggregated into an optimal gathering point, and then the air quality of the city group is then dynamically evaluated each year. According to the relevant data of the China Statistical Yearbook 2018, we applied this method to aggregate the air quality indices of the major cities in the Beijing-Tianjin-Hebei urban agglomeration from 2014 to 2017. Using the plant growth simulation algorithm (PGSA), the optimal assembly points were calculated to be of a higher accuracy, compared to the traditional mean value aggregation method. Finally, the air quality of the Beijing-Tianjin-Hebei urban agglomeration during each year was evaluated dynamically based on the obtained assembly points. The results show that the air quality of the urban agglomeration is ranked as follows: Y2016

Y2015

Y2017

Y2014.

关键词： Urban Agglomeration Air Quality Optimal Information Aggregation Dynamical Evaluation plant growth simulation algorithm

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A new approach for multiple attribute group decision-making based on interval neutrosophic sets

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第6期36卷 5929-5942页

作者： Qiu, Junda Li, Lei Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China Jiangnan Univ Business Sch 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China

The performance of interval neutrosophic sets (INSs) aggregated by resent operator is sensitive to special value (e.g. zero value). This paper proposes a novel algorithm for aggregating INSs and applies it to multiple attribute group decision-making. First, INSs are projected as point sets of a 2D space, thus the interval numbers of INSs and the 2D space points correspond one-to-one. Second, the plant growth simulation algorithm is employed to aggregate each point set and calculate the optimal rally points of each point set, therefore, the collective matrix is established by the optimal rally point set. Third, the proposed method achieved in the implementation of multiple attribute group decision-making with interval neutrosophic information. The simulation shows that the proposed aggregation method is insensitive to special value and achieve better performance than compared approach.

关键词： Interval neutrosophic set multiple attribute group decision-making plant growth simulation algorithm optimal rally point collective matrix

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Emergency decision-making combining cumulative prospect theory and group decision-making

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GRANULAR COMPUTING 2019年第1期4卷 39-52页

作者： Liu, Wenjing Li, Lei Jiangnan Univ Sch Business Wuxi 214122 Jiangsu Peoples R China

With respect to the characteristic of risk and the potential evolvement of scenarios in emergency management analysis, this study proposes an emergency decision-making method with interval probability based on cumulative prospect theory and group decision-making. Under emergency risk environment, there is a tremendous need to consider decision-maker's psychological behavior which affects the decision results. In addition, an emergency decision generally involves joint participation among departments, which inevitably brings about group decision-making. Therefore, aiming at decision problems in emergency management, this paper provides an algorithm of emergency group decision-making considering psychological behaviors. For illustration and verification, a numerical example and two comparisons are presented to demonstrate the effectiveness of proposed method. The contribution of this study is characterized by three aspects. First, cumulative prospect theory is introduced to quantify the impact of psychological behaviors. Second, group decision-making is considered as a think tank, which makes the decision more persuasive than single-person methods. Third, this study proposes a novel intelligent optimization algorithm, plant growth simulation algorithm, to integrate the different individual evaluations.

关键词： Emergency management Cumulative prospect theory Group decision-making Interval probability plant growth simulation algorithm

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Optimal capacitor placement in a radial distribution system using plant growth simulation algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第5期33卷 1133-1139页

作者： Rao, R. Srinivasas Narasimham, S. V. L. Ramalingaraju, M. JNT Univ Dept Elect Engn Kakinada 533003 India JNT Univ Dept Comp Sci & Engn Hyderabad 500072 Andhra Pradesh India

This paper presents a new and efficient approach for capacitor placement in radial distribution systems that determine the optimal locations and size of capacitor with an objective of improving the voltage profile and reduction of power loss. The solution methodology has two parts: in part one the loss sensitivity factors are used to select the candidate locations for the capacitor placement and in part two a new algorithm that employs plant growth simulation algorithm (PGSA) is used to estimate the optimal size of capacitors at the optimal buses determined in part one. The main advantage of the proposed method is that it does not require any external control parameters. The other advantage is that it handles the objective function and the constraints separately, avoiding the trouble to determine the barrier factors. The proposed method is applied to 10, 34, and 85-bus radial distribution systems. The solutions obtained by the proposed method are compared with other methods. The proposed method has outperformed the other methods in terms of the quality of solution. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Distribution systems Capacitor placement Loss reduction Loss sensitivity factors plant growth simulation algorithm

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A novel method for aggregating interval multiplicative comparison matrices and its application in ranking alternatives

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 3675-3684页

作者： Li, Jing Zhang, Yulin Southeast Univ Sch Econ & Management Nanjing 211189 Jiangsu Peoples R China

Aggregating individual interval multiplicative comparison matrices (IMCMs) and then ranking all the alternatives are very important in group decision making. However, the traditional methods may make the final result become overall average and does not always satisfy the Pareto optimal axiom. What's more, the ranking of the alternatives may rely on parameter or the possibility degree. In order to make the aggregated interval multiplicative comparison matrix (IMCM) and the ranking more reliable, this paper develops a novel method to aggregate interval multiplicative comparison matrices (IMCMs) and then applies logarithmic least squares method (LLSM) to rank alternatives. Firstly, DMs' interval multiplicative comparison matrices (IMCMs) are converted into two-dimensional coordinates. Next, the minimum Euclidean distance model is constructed and plant growth simulation algorithm (PGSA) is applied to find the aggregated points. Then the logarithmic least squares method (LLSM) is employed to derive the weight vectors and the ranking of alternatives can be obtained. Finally, numerical examples are provided to show the advantage and efficiency of the proposed method.

关键词： Group decision making interval multiplicative comparison matrices the minimum Euclidean distance model plant growth simulation algorithm

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Optimal DG Placement and Sizing for Power Loss Reduction in a Radial Distribution System using MPGSA and Sensitivity Index Method 11

Optimal DG Placement and Sizing for Power Loss Reduction in ...

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IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)

作者： Abd, Mohammed Kdair Cheng, S. J. Sun, H. S. Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan Hubei Province Peoples R China

ISBN: (纸本)9781467386449

This paper presents a new Maximum Power Loss Sensitivity (MPLS) method and Modified plant growth simulation algorithm (MPGSA) to find the optimal placement and size of Distributed Generation (DG). This study aims to reduce the total power loss and to improve the voltage profile. The proposed solution method has two steps: first, the MPLS is used to select the optimal position of the DG. Second, the MPGSA is used to find the optimal capacity of DG and then applied on an IEEE 33-bus system. However, this proposal can be implemented for renewable and non-renewable DGs. The results of the proposed algorithm were superior to the other methods in terms of minimizing the power loss and enhancing the voltage profile.

关键词： Distributed Generation Power Loss Voltage Profile plant growth simulation algorithm

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