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Bees Algorithm
Published in Kaushik Kumar, Divya Zindani, J. Paulo Davim, Optimizing Engineering Problems through Heuristic Techniques, 2020
Kaushik Kumar, Divya Zindani, J. Paulo Davim
Bees Algorithm has been inspired by the foraging behavior of the honeybees in nature. A bee colony, during the harvesting season, employs part of its population to scout the surrounding areas around the hives (Tereshko and Loengarov, 2005). Scout bees randomly searches for the food in the surrounding areas. Flower patches with abundant nectar are searched in particular by the scout bees. As such the extraction is easier and the patches are rich in sugar content.
Metaheuristic Algorithms-based Feature Selection Approach for Intrusion Detection
Published in Brij B. Gupta, Michael Sheng, Machine Learning for Computer and Cyber Security, 2019
Ammar Almomani, Mohammed Alweshah, Saleh Al Khalayleh, Mohammed Al-Refai, Riyadh Qashi
Pham et al. (2006) presented the Bees Algorithm which is inspired by bee behavior and can be used in many applications, like models of smart systems and social behavior [60]. In order to apply this algorithm, the distance between the solutions must be determined as this is a condition for application of the algorithm [61].[61]
Optimization of adhesive single-lap joints under bending moment
Published in The Journal of Adhesion, 2022
Mojtaba Hassan Vand, Hessam Abbaszadeh, Mohammad Shishesaz
Bees algorithm is a population-based search algorithm developed in 2005 by Pham et al. to solve optimization tasks.[18] This algorithm is the imitation of food foraging system used by bee colonies in nature. The performance of this algorithm consists of a number of steps. At first, the algorithm begins by scattering a population of scout bees randomly in the search space. The second step is the evaluation of the fitness of visited areas. Then, a number of the fittest places (mb) are chosen from the entire number of scouts (pb). The top solutions, which are called the elite sites (eb), are chosen from the fittest places (eb < mb < pb). The recruited foragers would search within the neighbourhood of the top places. The size of the neighbourhood search (ps) depends on different variables, and it shrinks in each iteration after reaching to a high number of iteration in order to avoid searching places with low efficient solutions and also to avoid entrapment in the local optimum points. A division of the new generation of scout bees (Neb) would search their neighbourhood of the fittest places particularly the elite sites. The remaining population of the new generation (Nmb) would search the remained space out of the neighbourhood of the fittest points (Nmb< Neb). In each neighbourhood, only the best scouts are chosen to form the next generation. This selection is not in nature, but the algorithm implies it to decrease the number of location search. The counter of the algorithm increases in one point by each repeat of these steps. The end criterion of the algorithm is the counter of loop repeats.
UKF-based enhancement and ROS implementation of 4-WDDMR localization with advanced control system
Published in Cogent Engineering, 2018
Abdulkader Joukhadar, Dalia Kass Hanna
The popularity of using robots as alternatives to human beings in the real world is becoming more common nowadays, especially in industry, medicine, entertainment, and military. These robots need means to think and to mimic the natural behavior of people. In this article, an artificial intelligence method is proposed to give robots thinking ability to find their way in buildings. The method presented is based on the Bees Algorithm which simulates foraging behavior of real honey bees. The proposed method is implemented as a computer program designed to find the shortest path in a building. This path is planned in a way to avoid collision with stationary barriers and moving objects such as other robots.
Using the Bees Algorithm for wheeled mobile robot path planning in an indoor dynamic environment
Published in Cogent Engineering, 2018
Ahmed Haj Darwish, Abdulkader Joukhadar, Mariam Kashkash
The popularity of using robots as alternatives to human beings in the real world is becoming more common nowadays, especially in industry, medicine, entertainment and military. These robots need means to think and to mimic the natural behavior of people. In this paper, an artificial intelligence method is proposed to give robots thinking ability to find their way in buildings. The method presented is based on the Bees Algorithm which simulates foraging behavior of real honey bees. The proposed method is implemented as a computer program designed to find the shortest path in a building. This path is planned in a way to avoid collision with stationary barriers and moving objects such as other robots.