A Transition Control Mechanism for Artificial Bee Colony (ABC) Algorithm

Abstract

Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-divided structure, the ABC algorithm has been successfully applied to different types of optimization problems. However, some assumptions that are made with the purpose of reducing implementation difficulties about the sophisticated behaviours of employed, onlooker, and scout bees still require changes with the more literal procedures. In this study, the ABC algorithm and its well-known variants are powered by adding a new control mechanism in which the decision-making process of the employed bees managing transitions to the dance area is modeled. Experimental studies with different types of problems and analysis about the parallelization showed that the newly proposed approach significantly improved the qualities of the final solutions and convergence characteristics compared to the standard implementations of the ABC algorithms.

On the performance of quick artificial bee colony algorithm for dynamicdeployment of wireless sensor networks

“…CS is 40, the total number of function evaluations is 2000, and for the qABC, r is 1. In order to assess the CPU times of the algorithms considering different dimensions, they were executed with different numbers of mobile sensors (25,50, and 100 mobile sensors). Other parameters were set as in the previous experiments.…”

“…Performance of the qABC was compared with some other optimization methods including the ABC, a parallel ABC (pABC) [24], a cooperative parallel ABC (coop-pABC) [24], a version of the ABC powered by a transition control mechanism (tlABC) [25], and a parallel version of the tlABC (p-tlABC) [25] on the dynamic deployment problem of WSNs. Details of these algorithms can be found in [24,25]. Since the results of the ABC, pABC, and coop-pABC algorithms were taken from [24] and the results of the tlABC and p-tlABC algorithms were taken from [25], similar parameter settings and scenarios were used in this comparison study.…”

“…Details of these algorithms can be found in [24,25]. Since the results of the ABC, pABC, and coop-pABC algorithms were taken from [24] and the results of the tlABC and p-tlABC algorithms were taken from [25], similar parameter settings and scenarios were used in this comparison study. The binary detection model is considered with 100 mobile sensors, r d is 7 m, and the area of interest is 10000 m 2 with 100 m length and 100 m width.…”

“…Aslan applied a parallel ABC and a cooperative parallel ABC to this problem by using a binary detection model and a WSN consisting of only mobile sensors [24]. He also proposed a new modified ABC by using a transition control mechanism and used it as a serial version and a parallel version for solving the dynamic deployment problem of WSNs that include mobile sensors with a binary detection model [25]. Yu et al proposed a sensor deployment algorithm that is based on a modified ABC in which the updating equation of the onlooker bees and scout bees of the original ABC is changed, and 100 mobile sensors are used in the WSN for an ideal room [26].…”

Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

“…The main idea is to create the multi agent system which is the colony of artificial bees to be able to efficiently solve hard combinatorial optimization problems. Therefore, ABC has been successfully employed in optimizations problems like data-mining problem [4,5], job shop scheduling [6,7], binary optimization [8][9][10], travelling salesman [11], biochemical networks [12], engineering optimization [13,14], image processing [15][16][17], as well as scheduling problem in cloud computing [18][19][20][21].…”