DocumentCode :
2220495
Title :
Fireworks algorithm with covariance mutation
Author :
Yu, Chao ; Tan, Ying
Author_Institution :
The Key Laboratory of Machine Perception and Intelligence (Ministry of Education), Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, China, 100871
fYear :
2015
fDate :
25-28 May 2015
Firstpage :
1250
Lastpage :
1256
Abstract :
Fireworks algorithm is a novel swarm intelligence algorithm for solving optimization problems — the latest versions include the adaptive fireworks algorithm and the dynamic fireworks algorithm. However, the mutation operator in the former algorithm was ineffective, whereas there was no mutation operator available in the latter algorithm. In this paper, a mutation operator is proposed, dubbed as the covariance mutation (CM) operator. The CM operator utilizes the information of the sparks with better fitness values to generate potential sparks for finding the optima of functions with higher possibility. Therefore, we proposed the fireworks algorithm with covariance mutation (FWACM) and compared it with the most advanced fireworks algorithms. The experimental results show that FWACM is a significant improvement for fireworks algorithms.
Keywords :
Covariance matrices; Explosions; Gaussian distribution; Heuristic algorithms; Next generation networking; Silicon; Sparks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2015 IEEE Congress on
Conference_Location :
Sendai, Japan
Type :
conf
DOI :
10.1109/CEC.2015.7257032
Filename :
7257032
Link To Document :
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