This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strateg...This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity;the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution;and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.展开更多
In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strate...In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.展开更多
A reduction of truss topology design problem formulated by semidefinite optimization （SDO） is considered. The finite groups and their representations are introduced to reduce the stiffness and mass matrices of truss...A reduction of truss topology design problem formulated by semidefinite optimization （SDO） is considered. The finite groups and their representations are introduced to reduce the stiffness and mass matrices of truss in size. Numerical results are given for both the original problem and the reduced problem to make a comparison.展开更多
This paper presents a study of minimizing weight by optimizing different truss parts using finite element analysis and comparing Warren trusses with other trusses. The aim of the optimization is to find a light design...This paper presents a study of minimizing weight by optimizing different truss parts using finite element analysis and comparing Warren trusses with other trusses. The aim of the optimization is to find a light design. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Applicable Eurocode 3 design conditions are presented, which provide the constraints for the problem. Steel truss is a preferred solution in large-span roof structures due to its good attributes, such as being lightweight and durable. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Constant spans of the trusses have been considered, and each truss has been subjected to the same types of load cases. The top chord member load has been kept constant in each truss at 2 kN/m. Two sets of load conditions are taken as the self-weight of the truss and the snow load, but the structure is calculated by the load combination. The structural steel trusses were optimized using the design optimization tool as a first-order optimization method in RFEM, and it was extended to compare the most suitable truss geometry for the minimum weight. Finally, it is concluded that the Warren truss has a higher stiffness-to-weight ratio than other trusses after optimization. The goal of this study was to analyze all trusses and ensure that the structural stress is less than the allowable stress and that the deflection is less than the allowable deflection. The span and height are constant in all cases because they have no impact on the weight increase;only the position of the rods and cross-section size affect the building’s ability to withstand loads and weight increases. In this paper, a finite element analysis (FEA)-based optimization technique is proposed for the optimization of a light design that is constrained by allowable stresses and deflections. For this purpose, there have been studies on sizing optimization to minimize the mass of different steel truss roof system types both in the past and today. For this purpose, weight design and analysis of the optimum weight are carried out on ten different structural systems.展开更多
Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Corona...Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection Algorithm(CMPA),is proposed based on the virus transmission of COVID-19.The main inspiration for the CMPA originated from human self-protection behavior against COVID-19.In CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune stage.Notably,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization algorithms.This study simulates the self-protection behavior mathematically and offers an optimization algorithm.The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design problems.The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms.Further,the CMPA is performed to identify the parameters of the main girder of a gantry crane.Results show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.展开更多
In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLS...In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.展开更多
基金supported by the National Science Foundation of China under Grant No.U21A20464,62066005Innovation Project of Guangxi University for Nationalities Graduate Education under Grant gxun-chxs2021058.
文摘This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity;the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution;and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.
基金the National Natural Science Foundation of China(Grant No.51305372)the Open Fund Project of the Transportation Infrastructure Intelligent Management and Maintenance Engineering Technology Center of Xiamen City(Grant No.TCIMI201803)the Project of the 2011 Collaborative Innovation Center of Fujian Province(Grant No.2016BJC019).
文摘In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.
基金Project supported by the National Natural Science Foundation of China (Grant No.10771133)the Research Fundation for the Doctoral Program of Higher Education (Grant No.200802800010)the Key Disciplines of Shanghai Municipality (GrantNo.s30104)
文摘A reduction of truss topology design problem formulated by semidefinite optimization （SDO） is considered. The finite groups and their representations are introduced to reduce the stiffness and mass matrices of truss in size. Numerical results are given for both the original problem and the reduced problem to make a comparison.
文摘This paper presents a study of minimizing weight by optimizing different truss parts using finite element analysis and comparing Warren trusses with other trusses. The aim of the optimization is to find a light design. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Applicable Eurocode 3 design conditions are presented, which provide the constraints for the problem. Steel truss is a preferred solution in large-span roof structures due to its good attributes, such as being lightweight and durable. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Constant spans of the trusses have been considered, and each truss has been subjected to the same types of load cases. The top chord member load has been kept constant in each truss at 2 kN/m. Two sets of load conditions are taken as the self-weight of the truss and the snow load, but the structure is calculated by the load combination. The structural steel trusses were optimized using the design optimization tool as a first-order optimization method in RFEM, and it was extended to compare the most suitable truss geometry for the minimum weight. Finally, it is concluded that the Warren truss has a higher stiffness-to-weight ratio than other trusses after optimization. The goal of this study was to analyze all trusses and ensure that the structural stress is less than the allowable stress and that the deflection is less than the allowable deflection. The span and height are constant in all cases because they have no impact on the weight increase;only the position of the rods and cross-section size affect the building’s ability to withstand loads and weight increases. In this paper, a finite element analysis (FEA)-based optimization technique is proposed for the optimization of a light design that is constrained by allowable stresses and deflections. For this purpose, there have been studies on sizing optimization to minimize the mass of different steel truss roof system types both in the past and today. For this purpose, weight design and analysis of the optimum weight are carried out on ten different structural systems.
基金supported by Henan Natural Science Foundation,No.222300420168,Yongliang YuanScience and Technology Plan Project of Henan Province,No.222102210182,Jianji Ren+3 种基金National Natural Science Foundation of China,No.52005081,Xiaokai MuNatural Science Foundation of Henan Polytechnic University,B2021-31,Yongliang YuanNonlinear equipment dynamics team of Henan Polytechnic University,T2019-5,Junkai FanFundamental Research Funds for the Universities of Henan Province,NSFRF220415,Yongliang Yuan.
文摘Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection Algorithm(CMPA),is proposed based on the virus transmission of COVID-19.The main inspiration for the CMPA originated from human self-protection behavior against COVID-19.In CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune stage.Notably,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization algorithms.This study simulates the self-protection behavior mathematically and offers an optimization algorithm.The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design problems.The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms.Further,the CMPA is performed to identify the parameters of the main girder of a gantry crane.Results show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.
基金National Natural Science Foundation of China,Grant No.52375264.
文摘In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
