查询结果:   王培崇.融合佳点集机制的动态搜索烟花爆炸搜索算法[J].计算机应用与软件,2015,32(8):248 - 251,299.
中文标题
融合佳点集机制的动态搜索烟花爆炸搜索算法
发表栏目
算法
摘要点击数
613
英文标题
DYNAMIC FIREWORKS EXPLOSION SEARCH ALGORITHM INTEGRATING GOOD-POINT SET
作 者
王培崇 Wang Peichong
作者单位
石家庄经济学院信息工程学院 河北 石家庄 050031 中国矿业大学(北京)机电与信息工程学院 北京 100083    
英文单位
School of Information Engineering,Shijiazhuang University of Economics, Shijiazhuang 050031, Hebei,China School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China    
关键词
群体智能 烟花爆炸搜索 动态随机搜索 佳点集
Keywords
Swarm intelligence Fireworks explosion search Dynamic random search Good-point set
基金项目
河北省科技攻关基金项目(13214711);石家庄经济学院预研基金项目(syy201310)
作者资料
王培崇,副教授,主研领域:智能信息处理。 。
文章摘要
为了克服烟花爆炸搜索算法容易早熟的弱点,提高其求解性能,提出一种融合佳点集变异机制的动态搜索烟花爆炸算法。首先为了提高算法的求解精度,每一次迭代过程均针对当前最佳个体执行动态随机搜索,加强对当前最佳的局部搜索。另一方面,当种群的拥挤程度超越设定的阈值λ时,除保留10%的优秀个体外,其余个体基于佳点集机制进行重新初始化,帮助种群摆脱局部最优的约束。最后,在6个Benchmark函数上的实验表明,该算法能快速收敛、克服早熟,并且具有较佳的鲁棒性。
Abstract
For overcoming the weakness of firework explosion search (FES) algorithm in being prone to prematurity and improving its solution performance, we proposed in this paper a dynamic firework explosion search algorithm which integrates the mutation mechanism of good-point set. First, in order to improve the precision of solution, every iteration process was targeted at current best individual to execute dynamic random search and this enhanced the local search for the current best. On the other hand, when the overcrowding degree of population exceeded the preset threshold λ, all the individuals, except 10% excellent ones remained, were to reinitialise based on good-point set mechanism to help the population get rid of the constraint from local optimum. In the end, the experiments on six classical Benchmark functions demonstrated that the improved FES algorithm could fast converge, prevented the prematurity and had better robustness.
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