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

Ant inspired algorithms have 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

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