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按概率突跳的改进微粒群优化算法 被引量：6

Modified particle swarm optimization via probabilistic leap
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摘要 在基本微粒群优化算法(PSO)的社会心理学分析基础之上,提出了一种改进的微粒群优化算法,该算法中引入了一个新的参数,改写了原算法中粒子飞翔的速度公式,使粒子飞行时以一定概率在解空间内改变飞翔的距离和方向———突跳。对5个标准测试函数的优化结果表明,合理地选取新参数的大小,新算法能大幅度降低达到最优值所需要的进化代数,同时提高算法的收敛率,尤其是对高维复杂函数的优化效果更明显。 Based on the social psychology analysis of particle swarm optimization （PSO）, a modified PSO was proposed. In this PSO a new parameter was introduced and the formula for flying velocity of the particle was modified, so that the event of a particle to leap stochastically to a different point can be taken into account. The simulations for 5 benchmark functions indicate that, when the new parameter is selected properly, the modified PSO can improve the search ability including efficiency and convergence dramatically, especially for high dimensional and complex functions.
作者 姜海明 谢康 王亚非
机构地区 电子科技大学光电信息学院
出处 《吉林大学学报（工学版）》 EI CAS CSCD 北大核心 2007年第1期141-145,共5页 Journal of Jilin University:Engineering and Technology Edition
基金 国家自然科学基金杰出青年科学基金资助项目(60588502) 国家自然科学基金青年科学基金资助项目(60607005) 四川省科技攻关计划项目(2006z02-010-3) 电子科技大学青年科技基金资助项目(JX04028)
关键词 计算机应用 突跳 微粒群优化 群智能 概率 computer application leapl particle swarm optimization swarm intelligence probability
分类号 TP301.6 [自动化与计算机技术—计算机系统结构] TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献12

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二级参考文献21

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共引文献55

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引证文献6

二级引证文献31

吉林大学学报（工学版）
2007年 第1期

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