An Improved Ant Colony Algorithm for Continuous Optimization Based on Levy Flight

Ant colony optimization (ACO) is proposed on the study of the foraging behavior of ants on the basis of the proposed and widely used in the optimization. However, it has some shortcoming such as longer time, hardly implement and local optimal etc. For overcoming the above shortcoming, combined with the characteristic of Levy flight, based on Levy flight ant colony optimization is proposed which used Levy flight instead of local search for improving the searching efficiency. In order to test the performance of the new algorithm, we apply it to 20 benchmark function test and compare it with GA, PSO, ACO and LFACO algorithms. The comparison result shows that LFACO is far better than the other three algorithms in quality.