引用本文:余冬华,郭茂祖,刘晓燕,等.改进选择策略的烟花算法[J].控制与决策,2020,35(2):389-395
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改进选择策略的烟花算法
余冬华1, 郭茂祖1,2,3, 刘晓燕1, 刘国军1
(1. 哈尔滨工业大学计算机科学与技术学院,哈尔滨150001;2. 北京建筑大学电气与信息工程学院,北京100044;3. 建筑大数据智能处理方法研究北京市重点实验室,北京100044)
摘要:
烟花算法(FWA)中的选择策略直接影响其收敛效率、收敛精度、对初值敏感性以及能否跳出局部最优,对此,提出一种改进选择策略的烟花算法(ISSFWA).ISSFWA建立峰值火花和探索火花的概念,并提出基于$N-1$朵峰值火花和一朵探索火花充当下一代N朵烟花的选择策略.峰值火花兼顾了火花的适应度值及相对位置,保证选择全局最优火花及峰值火花邻域内的局部最优火花,同时避免重复选择搜索能力相似的火花,而基于最远距离的探索火花可以增强全局探索能力.在10次标准及增加位置偏移的测试函数实验中,ISSFWA在最优适应度值方面优于PSO、GA、FWA;在平均适应度值方面优于PSO和FWA,略劣于GA.这一结果表明,ISSFWA能够增强寻找最优解的能力,降低对初值的敏感性,并提升搜索效率.
关键词:  群体智能  最优化  烟花算法  选择策略  峰值火花
DOI:10.13195/j.kzyjc.2018.0785
分类号:TP18
基金项目:国家自然科学基金项目(61571163,61532014,61671189,91735306);国家重点研发计划课题(2016YFC 0901902).
An improved selection strategy of firework algorithm
YU Dong-hua1,GUO Mao-zu1,2,3,LIU Xiao-yan1, LIU Guo-jun1
(1. School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China;2. School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;3. Beijing Key Laboratory of Intelligent Processing for Building Big Data,Beijing 100044,China)
Abstract:
The selection strategy is an import step of the fireworks algorithm(FWA), which directly affects the convergence efficiency, the convergence accuracy, the sensitivity to the initial value and the ability to jump out of the local optimal. Therefore, an improved selection strategy of the firework algorithm(ISSFWA) is proposed, which establishes the concept of spark peak and exploration spark. The improved selection strategy is proposed which selects peak sparks and selects the exploration spark as the next generation of fireworks. The peak sparks take into account the fitness values and relative position of sparks, which ensures that the global optimal spark and the local optimal spark in the neighborhood of the peak spark are selected. At the same time, it avoids duplication of sparks with similar search ability and keeps the firework with strong global exploration ability. And the exploration spark based on the largest distance enhances the ability of global exploration. In the 10 repetition test of standard and increased position deviation test function, the ISSFWA is superior to the PSO, GA, FWA in terms of the best fitness, and superior to the PSO, FWA in terms of average fitness, but slightly inferior to the GA. This result shows that the ISSFWA can enhance the ability of finding the optimal solution, reduce the sensitivity to the initial value, and improve the search efficiency.
Key words:  swarm intelligence  optimization  firework algorithm  selection strategy  peak spark

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