| 基于差分进化的量子粒子群优化算法的研究 |
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| 引用本文： | 留黎钦,孙波,王保云,张萍.基于差分进化的量子粒子群优化算法的研究[J].延边大学理工学报,2019,0(2):141-144. |
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| 作者姓名： | 留黎钦 孙波 王保云 张萍 |
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| 作者单位： | ( 1.莆田学院 信息工程学院, 福建 莆田 351100; 2.南京邮电大学 通信与信息工程学院, 江苏 南京 210003 ) |
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| 摘 要： | 为了提高量子粒子群算法(QPSO)的性能,利用差分进化对量子粒子群算法进行了优化.该优化算法(DE -QPSO)在粒子更新过程中,首先通过添加一个扰动来产生一个变异粒子,然后对变异粒子进行交叉操作产生新的试验粒子,最后对试验粒子进行选择操作,确定进入下一次迭代的个体.用5种标准测试函数对DE -QPSO、QPSO和 粒子群算法(PSO)的性能进行对比测试,结果表明DE-QPSO算法的性能明显优于PSO和QPSO算法,具有较好的应用价值.
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| 关 键 词： | 粒子群算法 量子粒子群算法 差分进化算法 |
Research on a quantum particle swarm optimization algorithm based on differential evolution |
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| LIU Liqin,SUN Bo,WANG Baoyun,ZHANG Ping.Research on a quantum particle swarm optimization algorithm based on differential evolution[J].Journal of Yanbian University (Natural Science),2019,0(2):141-144. |
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| Authors: | LIU Liqin SUN Bo WANG Baoyun ZHANG Ping |
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| Affiliation: | ( 1.College of Information Engineering, Putian University, Putian 351100, China; 2.College of Communication & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China ) |
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| Abstract: | In order to improve the performance of QPSO, differential evolution is used to optimize QPSO. Firstly, a disturbance is added to generate a mutant particle, and then the mutant particles are cross-operated to generate new experimental particles, and finally the test particles are selected to select the individual for the next iteration. The performance of the optimized algorithm(DE-QPSO), PSO and QPSO proposed in this paper is compared and tested with five kinds of standard test functions. The results show that the performance of DE-QPSO is obviously better than that of PSO and QPSO, and has good application value. |
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| Keywords: | particle swarm optimization quantum-behaved PSO differential evolution |
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