摘要： 为克服粒子群优化算法早熟收敛及粒子在进化过程中缺乏方向指导的问题，采用量子技术及免疫机制，提出一种自适应免疫量子粒子群优化算法。针对其计算量大、耗时长的缺点，结合已有的并行计算技术，构造该算法的并行计算方法。仿真实验结果表明，该并行算法在搜索能力和运行时间方面具有较好的性能。
关键词:
粒子群优化算法,
量子技术,
免疫机制,
并行计算
Abstract: In order to escape from premature convergence and lack of good direction in particles, the evolutionary process, quantum technology and immunologic mechanism are employed, and an adaptive immune quantum-behaved Particle Swarm Optimization(PSO) algorithm is provided. Meanwhile, according to larger calculation and longer consumed time, parallel computation technology is introduced into the provided algorithm. Simulation experiments show that the PSO algorithm has better performance.
Key words:
Particle Swarm Optimization(PSO) algorithm,
quantum technology,
immunologic mechanism,
parallel computation
中图分类号:
李红婵, 朱颢东. 并行自适应免疫量子粒子群优化算法[J]. 计算机工程, 2011, 37(5): 221-223.
LI Gong-Chan, SHU Hao-Dong. Parallel Adaptive Immune Quantum-behaved Particle Swarm Optimization Algorithm[J]. Computer Engineering, 2011, 37(5): 221-223.