A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems a...A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.展开更多
A dynamic programming-sequential quadratic programming(DP-SQP)combined algorithm is proposed to address the problem that the traditional continuous control method has high computational complexity and is easy to fall ...A dynamic programming-sequential quadratic programming(DP-SQP)combined algorithm is proposed to address the problem that the traditional continuous control method has high computational complexity and is easy to fall into local optimal solution.To solve the globally optimal control law sequence,we use the dynamic programming algorithm to discretize the separation control decision-making process into a series of sub-stages based on the time characteristics of the separation allocation model,and recursion from the end stage to the initial stage.The sequential quadratic programming algorithm is then used to solve the optimal return function and the optimal control law for each sub-stage.Comparative simulations of the combined algorithm and the traditional algorithm are designed to validate the superiority of the combined algorithm.Aircraft-following and cross-conflict simulation examples are created to demonstrate the combined algorithm’s adaptability to various conflict scenarios.The simulation results demonstrate the separation deploy strategy’s effectiveness,efficiency,and adaptability.展开更多
One of the most interesting topics related to sequential quadratic programming algorithms is how to guarantee the consistence of all quadratic programming subproblems. In this decade, much work trying to change the fo...One of the most interesting topics related to sequential quadratic programming algorithms is how to guarantee the consistence of all quadratic programming subproblems. In this decade, much work trying to change the form of constraints to obtain the consistence of the subproblems has been done The method proposed by De O. Panto-ja J F A and coworkers solves the consistent problem of SQP method, and is the best to the authors’ knowledge. However, the scale and complexity of the subproblems in De O. Pantoja’s work will be increased greatly since all equality constraints have to be changed into absolute form A new sequential quadratic programming type algorithm is presented by means of a special ε-active set scheme and a special penalty function. Subproblems of the new algorithm are all consistent, and the form of constraints of the subproblems is as simple as one of the general SQP type algorithms. It can be proved that the new method keeps global convergence and local superhnear convergence.展开更多
文摘A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.
基金supported in part by the National Natural Science Foundation of China(Nos.61773202,52072174)the Foundation of National Defense Science and Technology Key Laboratory of Avionics System Integrated Technology of China Institute of Aeronautical Radio Electronics(No.6142505180407)+1 种基金the Open Fund for Civil Aviation General Aviation Operation Key Laboratory of China Civil Aviation Management Cadre Institute(No.CAMICKFJJ-2019-04)the National key R&D plan(No.2021YFB1600500)。
文摘A dynamic programming-sequential quadratic programming(DP-SQP)combined algorithm is proposed to address the problem that the traditional continuous control method has high computational complexity and is easy to fall into local optimal solution.To solve the globally optimal control law sequence,we use the dynamic programming algorithm to discretize the separation control decision-making process into a series of sub-stages based on the time characteristics of the separation allocation model,and recursion from the end stage to the initial stage.The sequential quadratic programming algorithm is then used to solve the optimal return function and the optimal control law for each sub-stage.Comparative simulations of the combined algorithm and the traditional algorithm are designed to validate the superiority of the combined algorithm.Aircraft-following and cross-conflict simulation examples are created to demonstrate the combined algorithm’s adaptability to various conflict scenarios.The simulation results demonstrate the separation deploy strategy’s effectiveness,efficiency,and adaptability.
基金Project partly supported by the National Natural Science Foundation of China.
文摘One of the most interesting topics related to sequential quadratic programming algorithms is how to guarantee the consistence of all quadratic programming subproblems. In this decade, much work trying to change the form of constraints to obtain the consistence of the subproblems has been done The method proposed by De O. Panto-ja J F A and coworkers solves the consistent problem of SQP method, and is the best to the authors’ knowledge. However, the scale and complexity of the subproblems in De O. Pantoja’s work will be increased greatly since all equality constraints have to be changed into absolute form A new sequential quadratic programming type algorithm is presented by means of a special ε-active set scheme and a special penalty function. Subproblems of the new algorithm are all consistent, and the form of constraints of the subproblems is as simple as one of the general SQP type algorithms. It can be proved that the new method keeps global convergence and local superhnear convergence.
基金supported by The National Natural Science Foundation of China(No61075121, 60935001)by the Fundamental Research Funds for the Central Universities of China(No3162460)
