Abstract
| Original language | English |
|---|---|
| Number of pages | 7 |
| Journal | International Journal of Modeling, Simulation, and Scientific Computing |
| DOIs | |
| Publication status | Published - 6 Jul 2018 |
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Keywords
- pseudo derivative evolutionary algorithm
- evolutionary algorithm
- convergence analysis
- entropy theory
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Pseudo derivative evolutionary algorithm and convergence analysis. / Yu, Yang; Wang, Zhongjie; Tianfield, Huaglory; Lu, Chengchao.
In: International Journal of Modeling, Simulation, and Scientific Computing, 06.07.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Pseudo derivative evolutionary algorithm and convergence analysis
AU - Yu, Yang
AU - Wang, Zhongjie
AU - Tianfield, Huaglory
AU - Lu, Chengchao
N1 - Acceptance in SAN AAM: 12m embargo
PY - 2018/7/6
Y1 - 2018/7/6
N2 - In this paper a novel evolutionary algorithm (EA), called pseudo derivative evolutionary algorithm (PDEA), is proposed. The basic idea of PDEA is to use pseudo derivative, which is obtained based on the information produced during the evolution, to help search the solution of optimization problem. The pseudo derivative drives the search process in a more informed direction. That makes PDEA different from the random optimization methods. The convergence of PDEA is first analyzed based on systems theory. The convergence condition of PDEA is then derived, though this condition is too strong to be satisfied. Next, this condition is relaxed based on entropy theory. Finally, performances of PDEA are evaluated on the benchmark functions and an adaptive liquid level control system of a surge tank. The numeric simulation results show that PDEA is capable of finding the solutions to the optimization problems with good accuracy, reliability, and speed.
AB - In this paper a novel evolutionary algorithm (EA), called pseudo derivative evolutionary algorithm (PDEA), is proposed. The basic idea of PDEA is to use pseudo derivative, which is obtained based on the information produced during the evolution, to help search the solution of optimization problem. The pseudo derivative drives the search process in a more informed direction. That makes PDEA different from the random optimization methods. The convergence of PDEA is first analyzed based on systems theory. The convergence condition of PDEA is then derived, though this condition is too strong to be satisfied. Next, this condition is relaxed based on entropy theory. Finally, performances of PDEA are evaluated on the benchmark functions and an adaptive liquid level control system of a surge tank. The numeric simulation results show that PDEA is capable of finding the solutions to the optimization problems with good accuracy, reliability, and speed.
KW - pseudo derivative evolutionary algorithm
KW - evolutionary algorithm
KW - convergence analysis
KW - entropy theory
U2 - 10.1142/S1793962318500411
DO - 10.1142/S1793962318500411
M3 - Article
JO - International Journal of Modeling, Simulation, and Scientific Computing
JF - International Journal of Modeling, Simulation, and Scientific Computing
SN - 1793-9623
ER -