CLC number: TP301
On-line Access: 2022-12-14
Received: 2022-05-30
Revision Accepted: 2022-12-17
Crosschecked: 2022-09-13
Cited: 0
Clicked: 1949
Citations: Bibtex RefMan EndNote GB/T7714
Kai MENG, Chen CHEN, Bin XIN. MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1828-1847.
@article{title="MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization",
author="Kai MENG, Chen CHEN, Bin XIN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="12",
pages="1828-1847",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200237"
}
%0 Journal Article
%T MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization
%A Kai MENG
%A Chen CHEN
%A Bin XIN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 12
%P 1828-1847
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200237
TY - JOUR
T1 - MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization
A1 - Kai MENG
A1 - Chen CHEN
A1 - Bin XIN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 12
SP - 1828
EP - 1847
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200237
Abstract: The sparrow search algorithm (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal optimization problems. Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced sparrow search algorithm (MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an adaptive parameter control strategy is designed to accommodate an adequate balance between exploration and exploitation. Finally, a hybrid disturbance mechanism is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering optimization problems. The results demonstrate the superiority of the MSSSA in addressing practical problems.
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