摘要
针对求解复杂优化问题时,黏菌算法(SMA)存在全局搜索能力较弱、容易陷入局部最优值等问题,提出一种多策略融合的改进黏菌算法(TSMA)。首先,采用Logistic混沌映射来初始化黏菌个体的位置,增加种群的多样性提高算法的收敛速度;然后,融合萤火虫算法(FA),利用萤火虫自身的亮度和吸引度更新位置公式,增加种群在中期的探索和开发能力;最后,在算法迭代后期,引入T分布变异对最优解进行变异,从而提高种群跳出局部最优解的能力,提升寻优准确度。通过18个基准函数的测试,并与其他经典算法进行对比,结果表明,TSMA在搜索过程中明显优于其他算法,从而验证了对黏菌算法改进的有效性。
In order to solve complex optimization problems,the slime mold algorithm(SMA)has weak global search ability and is easy to fall into local optimal value.A multi-strategy fusion improved slime mold algorithm(TSMA)was proposed.Firstly,Logistic chaotic mapping was used to initialize the location of slime mold to increase the diversity of the population and improve the convergence speed of the algorithm.Then,the firefly algorithm(FA)was integrated to update the location formula based on fireflies’s own brightness and attraction so as to increase the exploration and the population in the medium term.Finally,in the late iteration of the algorithm,the T distribution variation is introduced to mutate the optimal solution,so as to improve the ability of the population to jump out of the local optimal solution and improve the optimization accuracy.Through the test of 18 benchmark functions,and compared with other classical algorithms,the results show that TSMA is significantly better than other algorithms in the search process,thus verifying the effectiveness of the improved slime mold algorithm.
作者
高帅
杨战海
GAO Shuai;YANG Zhanhai(College of Mathematics and Computer Science,Yan’an University,Yan’an 716000,China)
出处
《延安大学学报（自然科学版）》
2024年第3期102-108,115,共8页
Journal of Yan'an University：Natural Science Edition
基金
陕西省科技计划项目(2023-JC-QN-0744)
延安大学横向项目(2003206990344)。
