Abstract
In the combinatorial optimization, the goal is to find the optimal object from a finite set of objects. From computational point of view the combinatorial optimization problems are hard to be solved. Therefore on this kind of problems usually is applied some metaheuristics. One of the most successful techniques for a lot of problem classes is metaheuristic algorithm Ant Colony Optimization (ACO). Some start strategies can be applied on ACO algorithms to improve the algorithm performance. We propose several start strategies when an ant chose first node, from which to start to create a solution. Some of the strategies are base on forbidding some of the possible starting nodes, for one or more iterations, because we suppose that no good solution starting from these nodes. The aim of other strategies are to increase the probability to start from nodes with expectations that there are good solutions starting from these nodes. We can apply any of the proposed strategy separately or to combine them. In this investigation InterCriteria Analysis (ICrA) is applied on ACO algorithms with the suggested different start strategies. On the basis of ICrA the ACO performance is examined and analysed.
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Acknowledgements
Work presented here is partially supported by the National Scientific Fund of Bulgaria under Grants DFNI-02-5/2014 “Intercriteria Analysis – A Novel Approach to Decision Making” and DFNI I02/20 “Efficient Parallel Algorithms for Large Scale Computational Problems”, and by the Polish-Bulgarian collaborative Grant “Parallel and Distributed Computing Practices”.
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Roeva, O., Fidanova, S., Paprzycki, M. (2018). Comparison of Different ACO Start Strategies Based on InterCriteria Analysis. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-319-59861-1_4
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DOI: https://doi.org/10.1007/978-3-319-59861-1_4
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