HBMOHoliday Beach Migration Observatory (Canada)
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References in periodicals archive ?
Some existing "Tuned" mask techniques which are, respectively, proposed by Zheng (GA [23], HBMO [25]) and Ye et al.
However, GSA still has the best optimization ability in five algorithms, its average fitness value is the maximum for the 3 images, and the average classification accuracy has exceeded 92%; although the average fitness value of GSA and HBMO is very similar, the standard deviation of fitness value by using GSA is the minimum for 3 images, which proves that GSA can more stably converge to the optimal solution.
Results are compared with some other mask based classification techniques optimized by GA, PSO, AIA, and HBMO. In general, it is observed that evolutionary algorithm and swarm intelligence algorithm can be well used to complete the task of texture feature classification.
Parameter Explanation Value N Number of antibodies 20 B Antibody elimination rate 0.3 [P.sub.c] Crossover ratio 0.8 [P.sub.m] Mutation ratio 0.01 Table 5: Parameters used in HBMO. Parameter Explanation Value Number ofqueens 1 [N.sub.Drone] Number of drones 20 [N.sub.Brood] Number of broods 10 [alpha] Decreasing factor 0.98 Table 6: Result of different algorithms for public texture images.
Mohapatra, "Navigational path planning of multi-robot using honey bee mating optimization algorithm (hbmo)," International Journal of Computer Applications, vol.
Adams, "Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation," Journal of the Franklin Institute, vol.
Marino, "Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs," Journal of Hydroinformatics, vol.
proposed MBO algorithm as honey bee mating optimization (HBMO) algorithm and implemented it on water resources management applications [18].
For 10, 50,100, and 1000 problem sizes of unconstrained numeric six benchmark functions, comparisons were made between test results of IMBO algorithm and the algorithms in literature, including DE, PSO, ABC [32], bee swarm optimization, (BSO) [33], bee and foraging algorithm (BFA) [34], teaching-learning-based optimization (TLBO) [35], bumble bees mating optimization (BBMO) [36] and honey bees mating optimization algorithm (HBMO) [36].
In Tables 9 and 10 showing the comparison of IMBO with LBO, HBMO, and BBMO for genotype (problem) size 50, it is seen that IMBO performs better than all the other algorithms.