摘要
【目的】本文研究海鸥算法求解最优化问题时存在的收敛速度慢、易陷入局部最优等缺点。【方法】首先融合Fuch混沌映射与精英反向学习策略来初始化海鸥种群,提高种群质量;其次根据余弦函数改进自身行为的特征参数A,将海鸥算法的线性搜索非线性化;最后通过加入莱维飞行机制增加海鸥飞行的随机性,对算法进一步优化。【结果】通过9个基准测试函数和3个工程设计优化问题对I-SOA(Improved-Seagull Optimization Algorithm)性能进行测试,实验结果表明:对于9个基准测试函数,I-SOA算法比标准SOA、PSO、GA算法在寻优精度和收敛速度上都要优越,尤其在求解f7、f9时均求得理论最优解0;对于3个工程设计优化问题,I-SOA算法相比于标准SOA算法寻优精度和收敛速度优势明显,相比于其他群智能优化算法的最优值,适应性和稳定性更强。【结论】I-SOA算法在基准测试函数和工程设计优化问题中均有优异的表现,证实了海鸥算法改进的有效性。
[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 efiectiveness of the improvement of the seagull algorithm.
作者
杨韬
戴健
YANG Tao;DAI Jian(Department of business administration,Liaoning Technical University,Huludao,Liaoning 125100,China)
出处
《数据与计算发展前沿》
CSCD
2022年第6期129-144,共16页
Frontiers of Data & Computing
基金
国家自然科学基金(71771111)
辽宁省教育厅高校科研基金项目(LJKZ0359)。
