Frontiers of Data and Computing ›› 2022, Vol. 4 ›› Issue (6): 129-144.

CSTR: 32002.14.jfdc.CN10-1649/TP.2022.06.012

doi: 10.11871/jfdc.issn.2096-742X.2022.06.012

• Technology and Application • Previous Articles    

Improvement of the Seagull Algorithm and Its Application in Engineering Design Optimization

YANG Tao(),DAI Jian*()   

  1. Department of business administration, Liaoning Technical University, Huludao, Liaoning 125100, China
  • Received:2021-12-07 Online:2022-12-20 Published:2022-12-20
  • Contact: DAI Jian E-mail:yangtao@lntu.edu.cn;2663069519@qq.com

Abstract:

[Objective] This paper addresses the problem that the seagull algorithm has the defects of slow convergence speed and easy to fall into local optimization. [Methods] Firstly, the seagull population is initialized by combining the Fuch chaotic mapping and elite reverse learning strategy to improve the population quality. Secondly, the characteristic parameter A of its behavior is improved according to the cosine function to make the linear search of Seagull algorithm nonlinear. Finally, by adding Levy flight mechanism to increase the randomness of seagull flight, the algorithm is further optimized. [Results] The performance of I-SOA is tested by nine benchmark functions and three engineering design optimization problems. The experimental results show that for the nine benchmark functions, the I-SOA algorithm is superior to the standard SOA, PSO, and GA algorithms in optimization accuracy and convergence speed. Especially when solving f7 and f9, the theoretical optimal solution 0 is obtained. For the three engineering design optimization problems, I-SOA algorithm has obvious advantages in optimization accuracy and convergence speed compared to the standard SOA algorithm, and is stronger in adaptability and stability compared with the optimal value of other swarm intelligence optimization algorithms. [Conclusions] I-SOA algorithm has a great performance in benchmark functions and engineering design optimization problems, which proves the effectiveness of the improvement of the seagull algorithm.

Key words: seagull algorithm, Fuch chaotic mapping, elite reverse learning strategy, cosine function, Levy flight