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 evolutional by using the average information entropy self-adaptively. Simulation study and performance comparison on Traveling Salesman Problem show that the improved algorithm can converge at the global optimum with a high probability. The work provides a new approach for solving the combinatorial optimization problems.
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