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Investigating Behavioural Diversity via Gaussian Heterogeneous Ant Colony Optimization for Combinatorial Optimization Problems

This paper introduces and analyzes a heterogeneous approach to ACO that implements different 'behavioral traits' within a population from a Gaussian distribution thus enabling each ant to have its own distinguished traits. Each ant will be pre-assigned with a pair of traits in relation to α, the relative importance of pheromone, and β, the relative importance of heuristics. Max-Min Ant System (MMAS) is used as the base algorithm in this study for solving the Travelling Salesman Problem (TSP) and Printed Circuit Board (PCB) drilling problem. Empirical results indicate the diversity introduced by the heterogeneous approach improved the algorithm's performance over other approaches and alleviate the parameter tuning process required in finding optimal parameter settings as well as tackling the management of exploration - exploitation within ACO.