An Improved Artificial Bee Colony Algorithm for the Task Assignment in Heterogeneous Multicore Architectures

Abstract

The Artificial Bee Colony (ABC) algorithm is a new kind of intelligent optimization algorithm. Due to the advantages of few control parameters, computed conveniently and carried out easily, ABC algorithm has been applied to solve many practical optimization problems. But the algorithm also has some disadvantages, such as low precision, slow convergence, poor local search ability. In view of this, this article proposed an improved method based on adaptive neighborhood search and the improved algorithm is applied to the task assignment in Heterogeneous Multicore Architectures. In the experiments, although the numbers of iteration decreases from 1000 to 900, the quality of solution has been improved obviously, and the times of expenditure is reduced. Therefore, the improved ABC algorithm is better than the original ABC algorithm in optimization capability and search speed, which can improve the efficiency of heterogeneous multicore architectures.