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Reinforcement learning for solution updating in Artificial Bee Colony.

ABSTRACT: In the Artificial Bee Colony (ABC) algorithm, the employed bee and the onlooker bee phase involve updating the candidate solutions by changing a value in one dimension, dubbed one-dimension update process. For some problems which the number of dimensions is very high, the one-dimension update process can cause the solution quality and convergence speed drop. This paper proposes a new algorithm, using reinforcement learning for solution updating in ABC algorithm, called R-ABC. After updating a solution by an employed bee, the new solution results in positive or negative reinforcement applied to the solution dimensions in the onlooker bee phase. Positive reinforcement is given when the candidate solution from the employed bee phase provides a better fitness value. The more often a dimension provides a better fitness value when changed, the higher the value of update becomes in the onlooker bee phase. Conversely, negative reinforcement is given when the candidate solution does not provide a better fitness value. The performance of the proposed algorithm is assessed on eight basic numerical benchmark functions in four categories with 100, 500, 700, and 900 dimensions, seven CEC2005's shifted functions with 100, 500, 700, and 900 dimensions, and six CEC2014's hybrid functions with 100 dimensions. The results show that the proposed algorithm provides solutions which are significantly better than all other algorithms for all tested dimensions on basic benchmark functions. The number of solutions provided by the R-ABC algorithm which are significantly better than those of other algorithms increases when the number of dimensions increases on the CEC2005's shifted functions. The R-ABC algorithm is at least comparable to the state-of-the-art ABC variants on the CEC2014's hybrid functions.

SUBMITTER: Fairee S

PROVIDER: S-EPMC6049945 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHoney bee colonies managed for agricultural pollination are highly dependent on human inputs, especially for disease control and supplemental nutrition. Hives are routinely fed artificial "pollen substitute" diets to compensate for insufficient nutritional forage in the environment. The aim of this study was to investigate the effects of different artificial diets in a northern California, US commercial beekeeping operation from August through February. This time period represents an extended forage dearth when supplemental nutrition is used to stimulate late winter colony growth prior to almond pollination in the early spring. A total of 144 honey bee colonies were divided into 8 feeding groups that were replicated at three apiary sites. Feeding groups received commercial diets (Global, Ultra Bee, Bulk Soft, MegaBee, AP23, Healthy Bees), a beekeeper-formulated diet (Homebrew), or a sugar negative control. Diets were analyzed for macronutrient and amino acid content then evaluated with respect to honey bee colony population size, average bee weight, nutrition-related gene expression, gut microbiota abundance, and pathogen levels.ResultsReplicated at three apiary sites, two pollen-containing diets (Global and Homebrew) produced the largest colonies and the heaviest bees per colony. Two diets (Bulk Soft and AP23) that did not contain pollen led to significantly larger colonies than a sugar negative control diet. Diet macronutrient content was not correlated with colony size or health biomarkers. The sum of dietary essential amino acid deficiencies relative to leucine content were correlated with average bee weight in November and colony size used for almond pollination in February. Nutrition-related gene expression, gut microbiota, and pathogen levels were influenced by apiary site, which overrode some diet effects. Regarding microbiota, diet had a significant impact on the abundance of Bifidobacterium and Gilliamella and trended towards effects on other prominent bee gut taxa.ConclusionsMultiple colony and individual bee measures are necessary to test diet efficacy since honey bee nutritional responses are complex to evaluate. Balancing essential amino acid content relative to leucine instead of tryptophan may improve diet protein efficiency ratios. Optimization of bee diets could improve feed sustainability and agricultural pollination efficiency by supporting larger, healthier honey bee colonies.

Project description:Honey bee (Apis mellifera) colonies continue to experience high annual losses that remain poorly explained. Numerous interacting factors have been linked to colony declines. Understanding the pathways linking pathophysiology with symptoms is an important step in understanding the mechanisms of disease. In this study we examined the specific pathologies associated with honey bees collected from colonies suffering from Colony Collapse Disorder (CCD) and compared these with bees collected from apparently healthy colonies. We identified a set of pathological physical characteristics that occurred at different rates in CCD diagnosed colonies prior to their collapse: rectum distension, Malpighian tubule iridescence, fecal matter consistency, rectal enteroliths (hard concretions), and venom sac color. The multiple differences in rectum symptomology in bees from CCD apiaries and colonies suggest effected bees had trouble regulating water. To ensure that pathologies we found associated with CCD were indeed pathologies and not due to normal changes in physical appearances that occur as an adult bee ages (CCD colonies are assumed to be composed mostly of young bees), we documented the changes in bees of different ages taken from healthy colonies. We found that young bees had much greater incidences of white nodules than older cohorts. Prevalent in newly-emerged bees, these white nodules or cellular encapsulations indicate an active immune response. Comparing the two sets of characteristics, we determined a subset of pathologies that reliably predict CCD status rather than bee age (fecal matter consistency, rectal distension size, rectal enteroliths and Malpighian tubule iridescence) and that may serve as biomarkers for colony health. In addition, these pathologies suggest that CCD bees are experiencing disrupted excretory physiology. Our identification of these symptoms is an important first step in understanding the physiological pathways that underlie CCD and factors impacting bee health.

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Reinforcement learning for solution updating in Artificial Bee Colony.

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