Improving Ant Colony Optimization Efficiency for Solving Large TSP Instances

TL;DR

This paper introduces a novel Focused Ant Colony Optimization (FACO) variant that enhances efficiency in solving large Traveling Salesman Problem instances by controlling solution differences and integrating local search, achieving high-quality solutions faster.

Contribution

The paper proposes the FACO algorithm with a new solution difference control mechanism, improving performance over existing ACO methods for large TSP instances.

Findings

FACO outperforms state-of-the-art ACOs on large TSP instances.

High-quality solutions within 1% of best-known results are found in under an hour.

FACO effectively integrates with local search for improved results.

Abstract

Ant Colony Optimization (ACO) is a family of nature-inspired metaheuristics often applied to finding approximate solutions to difficult optimization problems. Despite being significantly faster than exact methods, the ACOs can still be prohibitively slow, especially if compared to basic problem-specific heuristics. As recent research has shown, it is possible to significantly improve the performance through algorithm refinements and careful parallel implementation benefiting from multi-core CPUs and dedicated accelerators. In this paper, we present a novel ACO variant, namely the Focused ACO (FACO). One of the core elements of the FACO is a mechanism for controlling the number of differences between a newly constructed and a selected previous solution. The mechanism results in a more focused search process, allowing to find improvements while preserving the quality of the existing solution. An additional benefit is a more efficient integration with a problem-specific local search. Computational study based on a range of the Traveling Salesman Problem instances shows that the FACO outperforms the state-of-the-art ACOs when solving large TSP instances. Specifically, the FACO required less than an hour of an 8-core commodity CPU time to find high-quality solutions (within 1% from the best-known results) for TSP Art Instances ranging from 100000 to 200000 nodes.