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A Comparative Study on Genetic Algorithm and Ant Colony Optimization in Resource Location Optimization

Hang Zhou 1 Xiao-Bing Hu 1
1 CAUC-ENAC - Joint Research Center of Applied Mathematics for ATM
CAUC - Civil Aviation University of China, ENAC - Ecole Nationale de l'Aviation Civile
Abstract : The resource location optimization (RLO) is a typical combinatorial optimization problem, which has a wide application background. The locations of facilities are optimized in order to minimize the users’ total costs in terms of different considerations. A mathematical model for this kind of problems is established. Both genetic algorithm (GA) and ant colony optimization (ACO) are meta-heuristic evolutionary methods, and they are applicable to resolve the problem of resource location optimization. Then, which method, GA or ACO, is fundamentally more suitable to RLO? To answer this question, in this paper, a comparative study on these two methods are carried out. The conclusion is then tested in a case study. Both methods are applied for the optimization of city air terminal locations in a large city of China, which is based on the urban road network and the passenger distribution from a survey. Both methodological theory and experiences show that the ACO could achieve a better solution in solving RLO problems.
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Submitted on : Friday, January 8, 2021 - 8:30:14 PM
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Hang Zhou, Xiao-Bing Hu. A Comparative Study on Genetic Algorithm and Ant Colony Optimization in Resource Location Optimization. SSCI 2020 IEEE Symposium Series on Computational Intelligence, Dec 2020, Canberra, Australia. pp.2932-2939 / ISBN:978-1-7281-2548-0, ⟨10.1109/SSCI47803.2020.9308224⟩. ⟨hal-03104395⟩



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