Crowding Population-based Ant Colony Optimization for the Multi-objective Travelling Salesman Problem


Ant inspired algorithms have recently gained popularity for use in multi-objective problem domains. One specific algorithm, Population-based ACO, which uses a population as well as the traditional pheromone matrix, has been shown to be effective at solving combinatorial multi-objective optimisation problems. This paper extends the Population-based ACO algorithm with a crowding population replacement scheme to increase the search efficacy and efficiency. Results are shown for a suite of multi-objective Travelling Salesman Problems of varying complexity.