The FCSE controlling equation of pinned thinwalled curve box was derived and the indeterminate problem of continuous thin-walled curve box with diaphragm was solved based on flexibility theory. With Bayesian statistic...The FCSE controlling equation of pinned thinwalled curve box was derived and the indeterminate problem of continuous thin-walled curve box with diaphragm was solved based on flexibility theory. With Bayesian statistical theory,dynamic Bayesian error function of displacement parameters of indeterminate curve box was founded. The corresponding formulas of dynamic Bayesian expectation and variance were deduced. Combined with one-dimensional Fibonacci automatic search scheme of optimal step size,the Powell optimization theory was utilized to research the stochastic identification of displacement parameters of indeterminate thin-walled curve box. Then the identification steps were presented in detail and the corresponding calculation procedure was compiled. Through some classic examples,it is obtained that stochastic performances of systematic parameters and systematic responses are simultaneously deliberated in dynamic Bayesian error function. The one-dimensional optimization problem of the optimal step size is solved by adopting Fibonacci search method. And the Powell identification of displacement parameters of indeterminate thin-walled curve box has satisfied numerical stability and convergence,which demonstrates that the presented method and the compiled procedure are correct and reliable.During parameters鈥？iterative processes,the Powell theory is irrelevant with the calculation of finite curve strip element（FCSE） partial differentiation,which proves high computation effciency of the studied method.展开更多
Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to ...Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to solve optimization problems.Further,for the complex higher dimensional problems,MFO is unable to make a good trade-off between global and local search.To overcome these drawbacks of MFO,in this work,an enhanced MFO,namely WF-MFO,is introduced to solve higher dimensional optimization problems.For a more optimal balance between global and local search,the original MFO’s exploration ability is improved by an exploration operator,namely,Weibull flight distribution.In addition,the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions found.Twenty-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected WF-MFO.Numerous popular algorithms and MFO versions have been compared to the achieved results.In addition,the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test,the Wilcoxon rank test,and convergence analysis.Compared to other methods,the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly,as shown by the experiments.Furthermore,the proposed WF-MFO has been used to the solution of two engineering design issues,with striking success.The improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data,statistical tests,and convergence performance.展开更多
基金supported by the National Natural Science Foundation of China (10472045, 10772078 and 11072108)the Science Foundation of NUAA(S0851-013)
文摘The FCSE controlling equation of pinned thinwalled curve box was derived and the indeterminate problem of continuous thin-walled curve box with diaphragm was solved based on flexibility theory. With Bayesian statistical theory,dynamic Bayesian error function of displacement parameters of indeterminate curve box was founded. The corresponding formulas of dynamic Bayesian expectation and variance were deduced. Combined with one-dimensional Fibonacci automatic search scheme of optimal step size,the Powell optimization theory was utilized to research the stochastic identification of displacement parameters of indeterminate thin-walled curve box. Then the identification steps were presented in detail and the corresponding calculation procedure was compiled. Through some classic examples,it is obtained that stochastic performances of systematic parameters and systematic responses are simultaneously deliberated in dynamic Bayesian error function. The one-dimensional optimization problem of the optimal step size is solved by adopting Fibonacci search method. And the Powell identification of displacement parameters of indeterminate thin-walled curve box has satisfied numerical stability and convergence,which demonstrates that the presented method and the compiled procedure are correct and reliable.During parameters鈥？iterative processes,the Powell theory is irrelevant with the calculation of finite curve strip element（FCSE） partial differentiation,which proves high computation effciency of the studied method.
文摘Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to solve optimization problems.Further,for the complex higher dimensional problems,MFO is unable to make a good trade-off between global and local search.To overcome these drawbacks of MFO,in this work,an enhanced MFO,namely WF-MFO,is introduced to solve higher dimensional optimization problems.For a more optimal balance between global and local search,the original MFO’s exploration ability is improved by an exploration operator,namely,Weibull flight distribution.In addition,the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions found.Twenty-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected WF-MFO.Numerous popular algorithms and MFO versions have been compared to the achieved results.In addition,the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test,the Wilcoxon rank test,and convergence analysis.Compared to other methods,the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly,as shown by the experiments.Furthermore,the proposed WF-MFO has been used to the solution of two engineering design issues,with striking success.The improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data,statistical tests,and convergence performance.
