An Improved Parallel Ant Colony Optimization Based on Message Passing Interface

Ant Colony Optimization (ACO) is recently proposed metaheuristic approach for solving hard combinatorial optimization problems. Parallel implementation of ACO can reduce the computational time obviously. An improved parallel ACO algorithm is proposed in this paper, which use dynamic transition probability to enlarge the search space by stimulating ants choosing new path at early stage; use polymorphic ant colony to improve convergence speed by local search and global search; use partially asynchronous parallel implementation, interactive multi-colony parallel and new information exchange strategy to improve the parallel efficiency. We implement the algorithm on the Dawn 4000L parallel computer using MPI and C language. The Numerical result indicates the algorithm proposed in this paper can improve convergence speed effectively with the fine solution quality.