Y. Hendrawan, M.R. Fauzy, C. Umam
One of the recently proposed algorithms in the field of nature-inspired algorithm is the Honey Bees Mating Optimization (HBMO) algorithm by Haddad and Afshar. The HBMO algorithm simulates the marriage behaviour of bees. The solutions are incrementally constructed by the HBMO algorithm. Therefore, the HBMO algorithm is a swarm-based constructive optimization algorithm. However, no study has reported the modification of HBMO for feature selection. Therefore, we try to develop a discrete honey bees mating optimization (DHBMO) as the modification of HBMO for solving discrete optimization problem especially feature selection to improve the performance of machine vision for predicting water stress in plants. We test the algorithm to solve feature selection problem using single and multi-objectives optimization to optimize water usage in plants for environmental sustainability. The results showed DHBMO has better performance compared to the existed nature-inspired optimization algorithms such as genetic algorithms (GA), simulated annealing (SA) and discrete-particle swarm optimization (discrete-PSO). © 2019 Institute of Physics Publishing. All rights reserved.
Department of Agricultural Engineering, University of Brawijaya, Jl. Veteran, Malang, Indonesia; Department of Industrial Management, Graduate School of Technology Management, Institut Teknologi Sepuluh November, Surabaya, Indonesia
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