An Improved Seagull Optimization Algorithm with Learning

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An Improved Seagull Optimization Algorithm with Learning

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[1]WANG Peichong,YIN Xinjie,LI Lirong.An Improved Seagull Optimization Algorithm with Learning[J].Journal of Zhengzhou University (Engineering Science),2022,43(06):8-14.[doi:10.13705/j.issn.1671-6833.2022.06.010]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 06 Page number: 8-14 Column: Public date: 2022-09-02

Title:: An Improved Seagull Optimization Algorithm with Learning

Author(s):: WANG Peichong¹; 2; YIN Xinjie¹; 2; LI Lirong³; 1.School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China;
2.Laboratory of AI and Machine Learning, Hebei GEO University, Shijiazhuang 050031, China;
3.School of Art, Hebei GEO University, Shijiazhuang 050031, China

Keywords:: seagull optimization algorithm; learning mechanism; nonlinear parameter; opposition-based learning

CLC:: TP301. 6；O234

DOI:: 10.13705/j.issn.1671-6833.2022.06.010

Abstract:: To overcome the weakness of slow convergence, prematureness and low accuracy of seagull optimization algorithm (SOA) in solving high-dimensional problems，an improved SOA with learning (ISAOL)was proposed. A migration operator based on the difference between the Xi and the Xmwas designed, this could make Xi search wider solution spaces in early stage, and a nonlinear adaptive parameter A was introduced to ensure the algorithm suitable for the search of solution space of complex problems, which could prevent the algorithm from falling into local optimum too early. In the later stage, some elite individuals executed opposition based learning(OBL) to intensify the exploration of the space around the global optimal individual to improve the accuracy of solution. Ten unconstrained test functions in CEC2017 were selected to test the performance of the ISOA and compared with HPSO-TS, V-DVGA, DADE, CMA-ES and others. Testing results of this experiments showed that the ISOAL had higher accuracy and stability than other algorithms. Finally, experiments was carried out by using the tension spring problem. The results showed that the total cost of the spring , the coil diameter and the average diameter of the spring obtained by the ISOAL were reduced by 3.5% ,5.7% and 3.5% than SOA, respectively. ISOAL had the attributes of fast convergence, high accuracy and robustness, fitting to solve higher dimensional function optimization problem and engineering optimization problems with constraints.

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Last Update: 2022-10-03