bee colony
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Insects perfectly fit the flagship principle of animal research – 3R: to reduce (the number of animals), to replace (animals with alternative models) and to refine (methods). Bees have the most important advantages of a model organism: they cause minimal ethical controversy, they have a small and fully known genome, and they permit the use of many experimental techniques. Bees have a fully functional DNMT toolkit. Therefore, they are used as models in biomedical/genetic research, e.g. in research on the development of cancer or in the diagnostics of mental and neuroleptic diseases in humans. The reversion of aging processes in bees offers hope for progress in gerontology research. The cellular mechanisms of learning and memory coding, as well as the indicators of biochemical immunity parameters, are similar or analogous to those in humans, so bees may become useful in monitoring changes in behavior and metabolism. Bees are very well suited for studies on the dose of the substance applied to determine the lethal dose or the effect of a formula on life expectancy. Honeybees have proven to be an effective tool for studying the effects of a long-term consumption of stimulants, as well as for observing behavioral changes and developing addictions at the individual and social levels, as well as for investigating the effects of continuously delivering the same dose of a substance. The genomic and physiological flexibility of bees in dividing tasks among workers in a colony makes it possible to create a Single- Cohort Colony (SCC) in which peers compared perform different tasks. Moreover behavioral methods (e.g. Proboscis Extension Reflex – PER, Sting Extension Reflex – SER, free flying target discrimination tasks or the cap pushing response) make it possible to analyse changes occurring in honeybee brains during learning and remembering. Algorithms of actions are created based on the behavior of a colony or individual, e.g. Artificial Bee Colony Algorithm (ABCA). Honeybees are also model organisms for profiling the so-called intelligence of a swarm or collective intelligence. Additionally, they serve as models for guidance systems and aviation technologies. Bees have inspired important projects in robotics, such as B-droid, Robobee and The Green Brain Project. It has also been confirmed that the apian sense of smell can be used to detect explosive devices, such as TNT, or drugs (including heroin, cocaine, amphetamines and cannabis). This inconspicuous little insect can revolutionize the world of science and contribute to the solution of many scientific problems as a versatile model.

The electricity system is generally rapidly developing for covering various power demands with requiring a reliable and safe supply where the substructures are expanding further in generation systems, transmission systems, and distribution systems. However, the system must be run economically to access energy at a cost-effective level related to existing energy enterprises and energy consumption in the load which is represented periodically in the total costs of operations for all operating units. As a basis for its determination, the transmission of economic power within the technical limits applicable is taken into consideration. Environmental factors, on the other hand, are also an impediment to technical limitations. As a result, the operation's economic measure is expressed in the process of providing and selling energy to customers. These works use the Artificial Bees Colony algorithm to determine the scheduling of generating units using the basic principle of optimization to describe its relationship as an economic function. The IEEE-30 bus system is used as a basic model for system development. The analysis' findings show that the weighting factor scheme has an impact on the minimum total cost and that the combination of the electricity distribution process and environmental factors has implications for the operational financial condition and electricity production. The power output, in particular, is proportional to the cost of each generating unit.

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes’ rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.

Abstract A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier's prediction errors and allows for faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in the onlooker bee phase (exploitation phase)of ABC to boost performance by maintaining the balance between exploration and exploitation of ABC. Tuned the modified ABC algorithm by using Naïve Bayes (NB) classifiers to improve the further accuracy of the model. Independent Component Analysis (ICA) is used for dimensionality reduction. In the first step, the reduced dataset is optimized by using Modified ABC and after that, in the second step, the optimized dataset is used to train the NB classifier. Extensive experiments were performed for comprehensive comparative analysis of the proposed algorithm with well-known metaheuristic algorithms, namely Genetic Algorithm (GA) when used with the same framework for the classification of six high-dimensional cancer datasets. The comparison results showed that the proposed model with the CS algorithm achieves the highest performance as maximum classification accuracy with less count of selected genes. This shows the effectiveness of the proposed algorithm which is validated using ANOVA for cancer classification.