Comparison solving discrete space on flower pollination algorithm, PSO and GA

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dc.contributor.authorRathasamuth, Wanthanee
dc.contributor.authorNootyaskool, Supakit
dc.date.accessioned2026-08-06T10:13:40Z
dc.date.available2026-08-06T10:13:40Z
dc.date.issued2016-03-23
dc.description.abstractIn order to find an optimal solution of a research problem, the problem parameters are encoded in discrete space (e.g. bits or integers). Genetic algorithm (GA) performs the discrete space by binary chromosome. Particle swarm optimization (PSO) uses probability and sigmoid function to convert the next bird's velocity into binary bit. Flower pollination algorithm (FPA) is quite new and also has a few number of the paper implements to discrete space. This research uses a cut-off parameter applied on FPA. The main objective is to examine the performance of the algorithms on discrete space and calculate velocity of PSO as well as Levy fight movement of FPA when using discrete parameter. The experiment used eight-numerical functions to test conversion from a real value to a bit value and measure the effectiveness of each algorithm. Experiment result showed that FPA could perform better than PSO and GA.
dc.identifier.citation2016 8th International Conference on Knowledge and Smart Technology Kst 2016, 18-21, 2016
dc.identifier.doi10.1109/KST.2016.7440499
dc.identifier.other2-s2.0-84966570642
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/6839
dc.source2016 8th International Conference on Knowledge and Smart Technology Kst 2016
dc.subjectdiscrete space
dc.subjectflower pollination algorithm
dc.subjectgenetic algorithm
dc.subjectparticle swarm optimization
dc.titleComparison solving discrete space on flower pollination algorithm, PSO and GA
dc.typeConference Paper

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