TY - JOUR
T1 - MSSSA
T2 - a multi-strategy enhanced sparrow search algorithm for global optimization
AU - Meng, Kai
AU - Chen, Chen
AU - Xin, Bin
N1 - Publisher Copyright:
© 2022, Zhejiang University Press.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - Adaptive parameter control strategy
KW - Hybrid disturbance mechanism
KW - Optimization problems
KW - Sparrow search algorithm
KW - Swarm intelligence
KW - TP301
UR - http://www.scopus.com/inward/record.url?scp=85144414692&partnerID=8YFLogxK
U2 - 10.1631/FITEE.2200237
DO - 10.1631/FITEE.2200237
M3 - Article
AN - SCOPUS:85144414692
SN - 2095-9184
VL - 23
SP - 1828
EP - 1847
JO - Frontiers of Information Technology and Electronic Engineering
JF - Frontiers of Information Technology and Electronic Engineering
IS - 12
ER -