TY - GEN
T1 - A novel electrons drifting algorithm for non-linear optimization problems
AU - Liao, Jian Tang
AU - Yang, Hong Tzer
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology, Taiwan, under Grants MOST 105-3113-E-006-007.
PY - 2016/10/19
Y1 - 2016/10/19
N2 - In response to higher and higher dimensions and complexity of optimization problems in engineering applications, the optimization algorithms face more and more challenges. This paper proposes a novel electron drifting algorithm (e-DA) to avoid the common disadvantages, such as easy to trap in a local optimal point and sensitive to initial solutions, of existing methods. A simple example is addressed in the paper to make readers easily understand the executed processes. Some benchmark functions are used for testing the effectiveness of the proposed e-DA. Besides, the performance of e-DA is compared with the existing optimization algorithms, including particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony (ABC). Numerical results verify that the searching efficiency and capability of the proposed e-DA are enhanced and better than the existing algorithms.
AB - In response to higher and higher dimensions and complexity of optimization problems in engineering applications, the optimization algorithms face more and more challenges. This paper proposes a novel electron drifting algorithm (e-DA) to avoid the common disadvantages, such as easy to trap in a local optimal point and sensitive to initial solutions, of existing methods. A simple example is addressed in the paper to make readers easily understand the executed processes. Some benchmark functions are used for testing the effectiveness of the proposed e-DA. Besides, the performance of e-DA is compared with the existing optimization algorithms, including particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony (ABC). Numerical results verify that the searching efficiency and capability of the proposed e-DA are enhanced and better than the existing algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84997777012&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84997777012&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2016.7603167
DO - 10.1109/FSKD.2016.7603167
M3 - Conference contribution
AN - SCOPUS:84997777012
T3 - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
SP - 155
EP - 160
BT - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
A2 - Du, Jiayi
A2 - Liu, Chubo
A2 - Li, Kenli
A2 - Wang, Lipo
A2 - Tong, Zhao
A2 - Li, Maozhen
A2 - Xiong, Ning
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
Y2 - 13 August 2016 through 15 August 2016
ER -