37
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Vol.37, No.11
2017
11
Systems Engineering — Theory & Practice Nov., 2017
doi: 10.12011/1000-6788(2017)11-2983-12
: TP301.6
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Improved whale optimization algorithm for large scale
optimization problems
LONG Wen
1,2
, CAI Shaohong
1
, JIAO Jianjun
2
, TANG Mingzhu
3
,WUTiebin
4
(1. Key Laboratory of Economics System Simulation, Guizhou Universit y of Finance and Economics, Guiy ang 550025, China;
2. School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China; 3. School of
Energy and Power Engineering, Changsha University of Science and Engineering, Changsha 410114, China; 4. School of
Energy and Electrical Engineering, Hunan University of Humanities, Science and Technology, Loudi 417000, China)
Abstract An improved whale optimization algorithm (WOA) based on nonlinear convergence factor,
named IWOA, is proposed for solving large scale complicated optimization problems. In the proposed
algorithm, opposition-based learning strategy is used to initial the whale individuals’ position in the search
space, which strengthened the diversity of individuals in the global searching process. A novel nonlinearly
update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.
It then disturbed the current optimal individual by diversity mutation operator in the process of the search
so as to avoid the possibility of falling into local optimum. Simulation experiments were conducted on the
15 large scale (200, 500, and 1000 dimension) conventional test functions. The experimental results show
that the proposed IWOA has better performance in solution precision and convergence rate than other
comparison methods.
Keywords whale optimization algorithm; opposition-based learning strategy; nonlinear convergence fac-
tor; large scale optimization problem; diversity mutation
: 2016-09-01
v
:
(1977–),
,
,
,
,
:
, E-mail: lw227@mail.gufe.edu.cn.
Æ
:
(61463009, 61403046);
(
[2016]1022);
(2016SWBZD13);
(2016JJ3079)
Foundation item: National Natural Science Foundation of China (61463009, 61403046); Science and Technology Foundation
of Guizhou Province ([2016]1022); Joint Foundation of Guizhou University of Finance and Economics and Ministry of Commerce
(2016SWBZD13); Natural Science Foundation of Hunan Province (2016JJ3079)
w
:
,
,
,
.
Æ
[J].
, 2017, 37(11):
2983–2994.
: Long W, Cai S H, Jiao J J, et al. Improved whale optimization algorithm for large scale optimization problems[J].
Systems Engineering — Theory & Practice, 2017, 37(11): 2983–2994.
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