Paper Info
Title
GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions
Abstract
Considering the problems of slow convergence and easily getting into local optimum of intelligent optimization algorithms in finding the optimal solution to complex high-dimensional functions, we have proposed an improved invasive weed optimization (IIWO). Concrete adjustments include setting the newborn seeds per plant to a fixed number, changing the initial step and final step to adaptive one, and re-initializing the solution which exceeds the boundary value. Meanwhile, through applying the algorithm to the GPU platform, a parallel IIWO (PIIWO) based on GPU is obtained. The algorithm not only improves the convergence, but also strikes a balance between the global and local search capabilities. The simulation results of solving on the CEC' 2010 1000-dimensional (1000D) functions, have shown that, compared with other algorithms, our designed IIWO can yield better performance, faster convergence, higher accuracy and stronger robustness; whilst the PIIWO has significant speedup than the IIWO.
Year
DOI
Venue
2014
10.1109/ICNC.2014.6975875
ICNC
Keywords
DocType
ISSN
optimisation,parallel processing,1000D function,1000D functions,speedup,GPU parallel,graphics processing units,intelligent optimization algorithms,adaptive step,GPU-based variation,complex high-dimensional functions,local search capabilities,parallel IIWO,IIWO,invasive weed optimization,fixed seeds,GPU platform,parallel invasive weed optimization algorithm,global search capabilities
Conference
2469-8814
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Aijia Ouyang115919.34
Libin Liu2367.37
Kenli Li354058.66
Keqin Li42778242.13