OpenMP Genetic Algorithm for Continuous Nonlinear Large-Scale Optimization Problems

Author(s):  
A. J. Umbarkar
2015 ◽  
Vol 35 ◽  
pp. 214-226 ◽  
Author(s):  
Jayanthi Manicassamy ◽  
S. Sampath Kumar ◽  
Mohana Rangan ◽  
V. Ananth ◽  
T. Vengattaraman ◽  
...  

Author(s):  
Jie Guo ◽  
Zhong Wan

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.


2017 ◽  
Vol 59 ◽  
pp. 340-362 ◽  
Author(s):  
Prabhujit Mohapatra ◽  
Kedar Nath Das ◽  
Santanu Roy

Mathematics ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 521 ◽  
Author(s):  
Fanrong Kong ◽  
Jianhui Jiang ◽  
Yan Huang

As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.


Sign in / Sign up

Export Citation Format

Share Document