Abstract
Quantum-behaved particle swarm optimization (short in QPSO) is an improved version of particle swarm particle (short in PSO), and the performance is superior. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. In this paper, we implement QPSO on MapReduce model, propose MapReduce quantum-behaved particle swarm optimization (short in MRQPSO), and realize QPSO parallel and distributed, which the MapReduce model is a parallel computing programming model. In the experiments, the test results show that MRQPSO is more advanced both on performance of solution and time than QPSO.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 61272279, 61272282, 61371201, and 61203303), the National Basic Research Program (973 Program) of China (No. 2013CB329402), the Program for Cheung Kong Scholars and Innovative Research Team in University (No. IRT-15R53), and the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048).
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Li, Y., Chen, Z., Wang, Y., Jiao, L. (2016). Quantum-Behaved Particle Swarm Optimization Using MapReduce. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_22
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DOI: https://doi.org/10.1007/978-981-10-3614-9_22
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