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

In 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.