ACO for Continuous Function Optimization: A Performance Analysis

TL;DR

This paper analyzes how different parameter settings affect the performance of Ant Colony Optimization (ACO) in continuous function optimization, highlighting the importance of selection strategy, distance measure, and evaporation rate.

Contribution

It provides a comprehensive performance analysis of ACO parameters and demonstrates how proper tuning improves its effectiveness over other meta-heuristics.

Findings

Roulette Wheel Selection improves ACO performance.

Squared Euclidean distance measure outperforms others.

Proper evaporation rate tuning is crucial for optimal results.

Abstract

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based meta-heuristic algorithm inspired by the foraging behavior of the ants, is no different. Fundamentally, the ACO depends on the construction of new solutions, variable by variable basis using Gaussian sampling of the selected variables from an archive of solutions. A comprehensive performance analysis of the underlying parameters such as: selection strategy, distance measure metric and pheromone evaporation rate of the ACO suggests that the Roulette Wheel Selection strategy enhances the performance of the ACO due to its ability to provide non-uniformity and adequate diversity in the selection of a solution. On the other hand, the Squared Euclidean distance-measure metric offers better performance than other distance-measure metrics. It is observed from the analysis that the ACO is sensitive towards the evaporation rate. Experimental analysis between classical ACO and other meta-heuristic suggested that the performance of the well-tuned ACO surpasses its counterparts.