萤火虫算法
水准点(测量)
计算机科学
群体智能
高斯分布
趋同(经济学)
萤火虫协议
算法
早熟收敛
集合(抽象数据类型)
对象(语法)
人工智能
数学优化
数学
粒子群优化
地理
程序设计语言
物理
经济
动物
生物
量子力学
经济增长
大地测量学
出处
期刊:International Journal of Innovative Computing and Applications
[Inderscience Publishers]
日期:2019-01-01
卷期号:10 (1): 35-35
被引量:9
标识
DOI:10.1504/ijica.2019.100535
摘要
Firefly algorithm (FA), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimisation. However, the premature convergence has greatly affected the performance of original FA. To overcome this problem, we proposed a Gaussian bare-bones FA, named GBFA, in which each firefly moves to a Gaussian bare-bones method generated learning object rather than its better neighbours. The experiments are conducted on a set of widely used benchmark functions. Experimental results and comparison with the state-of-the-art FA variants have proved that the proposed algorithm is promising.
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