A Multi-cores Parallel Artificial Bee Colony Optimization Algorithm Based on Fork/Join Framework
There are lots of computationally intensive tasks in optimization process of Artificial Bee Colony (ABC) algorithm, which requires large CPU processing time. To improve optimization precision and performance of the ABC algorithm, a parallel Multi-cores Parallel ABC algorithm (MPABC) was proposed based on the Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee population’s diversity, improve the global convergence ability and avoid falling into the local optimum. The performance of the original serial ABC algorithm and the MPABC algorithm was analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve the speedup of 3.795 and the efficiency of 94.87% in solving complex problems. It can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process.
KeywordsParallel Artificial bee colony algorithm Fork/join framework
This work is supported by National Nature Science Foundation of China (Grant No. 61462058), Gansu Province Science and Technology Program (No. 1606RJZA004) and Gansu Data Engineering and Technology Research Center for Resources and Environment.
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