典型文献
MSSSA:a multi-strategy enhanced sparrow search algorithm for global optimization
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Kai MENG;Chen CHEN;Bin XIN
作者机构：
School of Automation,Beijing Institute of Technology,Beijing 100081,China;State Key Laboratory of Intelligent Control and Decision of Complex Systems,Beijing 100081,China
文献出处：
引用格式：
[1]Kai MENG;Chen CHEN;Bin XIN-.MSSSA:a multi-strategy enhanced sparrow search algorithm for global optimization)[J].信息与电子工程前沿（英文）,2022(12):1828-1847
A类：
MSSSA
B类：
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AB值：
0.60134
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