A self-tuning Firefly algorithm to tune the parameters of Ant Colony System (ACSFA)

TL;DR

This paper introduces a self-tuning framework combining the firefly algorithm with the ant colony system to automatically optimize parameters for solving symmetric TSP problems, eliminating manual tuning.

Contribution

It presents a novel self-tuning approach that automatically adjusts parameters of both the firefly algorithm and ant colony system for improved problem-solving performance.

Findings

The framework effectively tunes ACS parameters for symmetric TSP.

Statistical analysis confirms the superiority of the self-tuned ACS.

The method reduces manual effort in parameter selection.

Abstract

Ant colony system (ACS) is a promising approach which has been widely used in problems such as Travelling Salesman Problems (TSP), Job shop scheduling problems (JSP) and Quadratic Assignment problems (QAP). In its original implementation, parameters of the algorithm were selected by trial and error approach. Over the last few years, novel approaches have been proposed on adapting the parameters of ACS in improving its performance. The aim of this paper is to use a framework introduced for self-tuning optimization algorithms combined with the firefly algorithm (FA) to tune the parameters of the ACS solving symmetric TSP problems. The FA optimizes the problem specific parameters of ACS while the parameters of the FA are tuned by the selected framework itself. With this approach, the user neither has to work with the parameters of ACS nor the parameters of FA. Using common symmetric TSP problems we demonstrate that the framework fits well for the ACS. A detailed statistical analysis further verifies the goodness of the new ACS over the existing ACS and also of the other techniques used to tune the parameters of ACS.