A Modified Sine Cosine Algorithm for Numerical Optimization

被引:2
作者
Xiong, Yan [1 ,2 ]
Cheng, Jiatang [1 ]
机构
[1] Guilin Univ Technol, Educ Dept Guangxi Zhuang Autonomous Reg, Key Lab Adv Mfg & Automat Technol, Guilin 541006, Peoples R China
[2] Guilin Univ Technol, Coll Mech & Control Engn, Guilin 550001, Peoples R China
关键词
Sine Cosine algorithm; parameter adjustment; inertia weight; optimization;
D O I
10.1142/S1469026824500159
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Sine cosine algorithm (SCA) is a recently developed meta-heuristic method based on the characteristics of sine and cosine functions. However, SCA algorithm may suffer premature convergence in solving complex optimization problems. To mitigate this limitation, a modified SCA (MSCA) is developed to achieve an effective balance between exploration and exploitation. Specifically, the conversion parameter can be configured adaptively by using the individual fitness information in the evolution process to guide the individual search behavior. After that, an inertia weight is embedded in the position updating equation to further improve the search accuracy and accelerate the convergence speed. The performance of MSCA algorithm is investigated on 13 benchmark functions and the chaotic time series prediction problem. Experimental results demonstrate that MSCA algorithm is more effective than the other optimization methods.
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页数:16
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