Paper Info
Title
An Enhanced Brain Storm Sine Cosine Algorithm for Global Optimization Problems.
Abstract
The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBS-SCA), where an EBS strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies [individual update strategies (IUS): IUS-I and IUS-II] are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare the EBS-SCA with other metaheuristic algorithms. The experimental results validate that the proposed EBS-SCA achieves the overall best performance including the global search ability, convergence speed, and scalability.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2900486
IEEE ACCESS
Keywords
Field
DocType
Sine cosine algorithm (SCA),brain storm optimization (BSO),metaheuristic algorithm,global optimization
Convergence (routing),Mathematical optimization,Sine cosine algorithm,Premature convergence,Computer science,Storm,Optimization problem,Metaheuristic,Global optimization problem,Scalability,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chunquan Li19512.61
Zu Luo210.68
Zhenshou Song342.05
Feng Yang42615.37
Jinghui Fan5276.19
Peter Xiaoping Liu6115891.78