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InterCriteria Analysis of the Evaporation Parameter Influence on Ant Colony Optimization Algorithm: A Workforce Planning Problem

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Recent Advances in Computational Optimization (WCO 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 920))

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Abstract

Optimization of the production process is an important task for every factory or organization. A better organization can be done by optimization of the workforce planing. The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done. The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence. The problem is to select employers and to assign them to the jobs to be performed. The constraints of this problem are very strong and it is difficult to find feasible solutions. We apply Ant Colony Optimization Algorithm (ACO) to solve the problem. We investigate the algorithm performance by changing the evaporation parameter. The aim is to find the best parameter setting. To evaluate the influence of the evaporation parameter on ACO InterCriteria Analysis (ICrA) is applied. ICrA is performed on the ACO results for 10 problems considering average and maximum number of iterations needed to solve the problem. Five different values of evaporation parameter are used. The results show that ACO algorithm has best performance for two values of evaporation parameter – 0.1 and 0.3.

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Acknowledgements

Work presented here is partially supported by the National Scientific Fund of Bulgaria under Grant KP-06-N22/1 “Theoretical Research and Applications of InterCriteria Analysis” and by the European Union through the European structural and Investment funds Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program.

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Correspondence to Olympia Roeva .

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Roeva, O., Fidanova, S., Ganzha, M. (2021). InterCriteria Analysis of the Evaporation Parameter Influence on Ant Colony Optimization Algorithm: A Workforce Planning Problem. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. WCO 2019. Studies in Computational Intelligence, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-030-58884-7_5

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