A new algorithm is presented by using the ant colony algorithm based on genetic method （ACG） to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of componen...A new algorithm is presented by using the ant colony algorithm based on genetic method （ACG） to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of component, trail information and fitness. The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed. The genetic method is used to form new solutions from the solutions got by the ants. Best solutions are selected to update the seeds in the sets and trail information of the seeds. In updating the trail information, a diffusion function is used to achieve the diffuseness of trail information. The new algorithm is tested with 8 different benchmark functions.展开更多
In this paper, the continuously optimal location problem is considered. The strong convexity of the objective function, the Lipschitz continuity of the gradient of the objective function are proved. Furthermore, a var...In this paper, the continuously optimal location problem is considered. The strong convexity of the objective function, the Lipschitz continuity of the gradient of the objective function are proved. Furthermore, a variant of conjugate gradient method for continuously optimal location problem is presented and its global convergence is analyzed.展开更多
基金project supported by the National High-Technology Research and Development Program of China(Grant No.8632005AA642010)
文摘A new algorithm is presented by using the ant colony algorithm based on genetic method （ACG） to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of component, trail information and fitness. The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed. The genetic method is used to form new solutions from the solutions got by the ants. Best solutions are selected to update the seeds in the sets and trail information of the seeds. In updating the trail information, a diffusion function is used to achieve the diffuseness of trail information. The new algorithm is tested with 8 different benchmark functions.
基金The subject is supported by Natural Science Foundation of China( No
文摘In this paper, the continuously optimal location problem is considered. The strong convexity of the objective function, the Lipschitz continuity of the gradient of the objective function are proved. Furthermore, a variant of conjugate gradient method for continuously optimal location problem is presented and its global convergence is analyzed.
