摘要
In order to solve the premature convergence problem of the basic Ant Colony Optimization algorithm, a promising modification with changing index was proposed. The main idea of the modification is to measure the uncertainty of the path selection and evolution by using the average information entropy self-adaptively. Simulation study and perform-ance comparison on Traveling Salesman Problem show that the improved algorithm can converge at the global opti-mum with a high probability. The work provides a new approach for solving the combinatorial optimization problems, especially the NP-hard combinatorial optimization problems.
In order to solve the premature convergence problem of the basic Ant Colony Optimization algorithm, a promising modification with changing index was proposed. The main idea of the modification is to measure the uncertainty of the path selection and evolution by using the average information entropy self-adaptively. Simulation study and perform-ance comparison on Traveling Salesman Problem show that the improved algorithm can converge at the global opti-mum with a high probability. The work provides a new approach for solving the combinatorial optimization problems, especially the NP-hard combinatorial optimization problems.
