Adaptive ACO for Complicated Optimization Problems

摘要

To overcome the premature convergence deficiency of the basic Ant Colony Algorithm and find a method to deal with the continuous-space problem for ant algorithms, an improved Ant Colony Algorithm based on the information entropy was proposed. The main idea is to control the path selection and evolutional strategy by self-adjusting. Simulation study results in solving the NP-hard problems and continuous-space problem demonstrate its efficiency and robustness in solving the complicated combinatorial optimization problems.