Using hyper populated ant colonies for solving the TSP

Abstract

The paper discusses the application of hyper populated ant colonies to the well-known traveling salesman problem (TSP). The ant colony optimization (ACO) approach offers reasonably good quality solutions for the TSP, but it suffers from its inherent non-determinism and as a consequence the processing time is unpredictable. The paper tries to mitigate the problem by a substantial increase in the number of used ants. This approach is called ant hyper population and it could be obtained by increasing the number of ants in a single colony assigning more than one colony to solve the same task or both. In all cases the level of nondeterminism decreases and thus the number iterations could be reduced. Parallel implementation of the ACO makes it possible to reduce drastically the processing time. The paper compares two ways of implementation of the parallelism using the sockets or the RMI-remote method invocation mechanisms. The paper concentrates on the classical static version of the TSP, but preliminary experiments indicate that such an approach could be even more useful for dynamic TPSs.

Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem [Research Frontier]

“…It must be noted that there are some ACO algorithms that benefit from the use of less ants (e.g., the Ant Colony System [1]), resulting in less evaluations per algorithmic iteration, and some other ACO algorithms that benefit from the use of more ants (e.g., the hyper-populated ant colonies [26], [27]), resulting in more evaluations per algorithmic iteration. Therefore, the frequency of change is expressed in evaluations in the described dynamic benchmark framework.…”

Parallel Ant Colony Optimization for the Electric Vehicle Routing Problem

“…There are several opportunities for parallelization within the ACO framework, including ants constructing solutions independently from other ants in a single colony [10], [11] or in multiple colonies [3]. In fact, parallel ACO algorithms with multiple (independent) colonies have been successfully applied to several combinatorial optimization problems, including the travelling salesman problem [3], [8], the capacitated vehicle routing problem [12], and the quadratic assignment problem [13].…”