Abstract:
The Ant Colony Optimization (ACO) is a
metaheuristic algorithm used for combinatorial
optimization problems. It is a good choice for
many hard combinatorial problems because it is
more efficient and produces better solutions than
greedy algorithms. However, ACO is
computationally expensive and it can still trap in
local optima, take a long time to compute a
solution on large problem sets and premature
convergence problem. The main idea of the
modification is to limit the number of elements
choices to a sensible subset, or candidate list,
which can limit the selection scope of ants at
each step and thus substantially reduce the size
of search space and to measure the uncertainty
of the path selection and evolution by using the
information entropy self-adaptively. Simulation
study and performance comparison on Traveling
Salesman Problem show that the improved
algorithm can converge at global optimum with
a high probability. It also shows a faster
convergence to the solutions than the standard
algorithm.
