In this paper, hybrid ant colony optimization\n(HAntCO) approach in solving multi-skill resource-constrained\nproject scheduling problem (MS-RCPSP) has been presented.\nWe have proposed hybrid approach that links classical\nheuristic priority rules for project scheduling with ant\ncolony optimization (ACO). Furthermore, a novel approach\nfor updating pheromone value has been proposed based on\nboth the best and worst solutions stored by ants. The objective\nof this paper is to research the usability and robustness\nof ACO and its hybrids with priority rules in solving MSRCPSP.\nExperiments have been performed using artificially\ncreated dataset instances based on real-world ones. We published\nthose instances that can be used as a benchmark. Presented\nresults show that ACO-based hybrid method is an\nefficient approach. More directed search process by hybrids\nmakes this approach more stable and provides mostly better\nresults than classical ACO.
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