大规模优化问题的多核并行免疫协同粒子群算法

Proceedings of 2014 International Conference on Cloud Computing and Internet of Things Pub Date : 2014-12-01 DOI:10.1109/CCIOT.2014.7062513

Zhaohua Liu, Jingjing Zhao, Xiao-Hua Li, Weng-Hooi Tan

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引用次数: 3

摘要

针对大规模优化问题，提出了一种基于多核结构的并行协同多粒子群免疫优化算法M-PCPSO-I。设计了一种新的粒子内存信息共享方案，方便了种群空间中不同群体之间的通信。将从子群中选出的全局最优个体保存在领导集中，并使用改进的免疫克隆选择算子进行提升。M-PCPSO-I算法通过多核架构在共享内存计算机系统上并行实现。高维问题的结果验证了该算法具有良好的计算性能，并且通过多核并行化大大提高了计算效率。

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Multi-core based parallelized cooperative PSO with immunity for large scale optimization problem

A parallelized cooperative multiple particles swarm optimization algorithm with immunity mechanism based on the multi-core architecture is proposed for large scale optimization problem in this paper, named M-PCPSO-I. A novel memory information sharing scheme is designed for particles and facilitates communication among different swarms in the population space. The global best individuals selected from sub-swarms are saved in the leader set and promoted by using the improved immune clonal selection operator. The M-PCPSO-I algorithm is paralleling implementation on a share-memory computer system through the multi-core architecture. The high dimension problem results validated the proposed algorithm have good computational performance, and also the computational efficiency is greatly enhanced by multi-core parallelization.

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Proceedings of 2014 International Conference on Cloud Computing and Internet of Things

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