In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLS...In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.展开更多
针对移动机器人寻找最优路径问题,提出了一种融合无标度网络、自适应权重和黄金正弦算法变异策略的樽海鞘群算法BAGSSA(Adaptive Salp Swarm Algorithm with Scale-free of BA Network and Golden Sine)。首先,生成一个无标度网络来映...针对移动机器人寻找最优路径问题,提出了一种融合无标度网络、自适应权重和黄金正弦算法变异策略的樽海鞘群算法BAGSSA(Adaptive Salp Swarm Algorithm with Scale-free of BA Network and Golden Sine)。首先,生成一个无标度网络来映射跟随者的关系,增强算法全局寻优的能力,在追随者进化过程中集成自适应权重ω,以实现算法探索和开发的平衡;同时选用黄金正弦算法变异进一步提高解的精度。其次,对12个基准函数进行仿真求解,实验数据表明平均值、标准差、Wilcoxon检验和收敛曲线均优于基本樽海鞘群和其他群体智能算法,证明了所提算法具有较高的寻优精度和收敛速度。最后,将BAGSSA应用于移动机器人路径规划问题中,并在两种测试环境中进行仿真实验,仿真结果表明,改进樽海鞘群算法较其他算法所寻路径更优,并具有一定理论与实际应用价值。展开更多
针对基本樽海鞘群算法收敛速度慢、收敛精度低、易陷入局部最优的缺点,提出了一种融合黄金正弦混合变异的自适应樽海鞘群算法AGHSSA(Adaptive Salp Swarm Algorithm with Golden Sine Algorithm and Hybrid Mutation)。该算法引入了自...针对基本樽海鞘群算法收敛速度慢、收敛精度低、易陷入局部最优的缺点,提出了一种融合黄金正弦混合变异的自适应樽海鞘群算法AGHSSA(Adaptive Salp Swarm Algorithm with Golden Sine Algorithm and Hybrid Mutation)。该算法引入了自适应变化的权重因子以加强精英个体的引导作用,提升收敛速度与精度。通过黄金正弦算法优化领导者位置更新方式,增强算法的全局搜索和局部开发能力。融合邻域重心反向学习与柯西变异对最优个体位置进行扰动,提升算法跳出局部最优的能力。通过对12个基准测试函数进行仿真实验来评估改进算法的寻优能力,实验结果表明,改进算法能显著提升寻优速度和精度,并且具备较强的跳出局部最优的能力。展开更多
基金National Natural Science Foundation of China,Grant No.52375264.
文摘In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
文摘针对移动机器人寻找最优路径问题,提出了一种融合无标度网络、自适应权重和黄金正弦算法变异策略的樽海鞘群算法BAGSSA(Adaptive Salp Swarm Algorithm with Scale-free of BA Network and Golden Sine)。首先,生成一个无标度网络来映射跟随者的关系,增强算法全局寻优的能力,在追随者进化过程中集成自适应权重ω,以实现算法探索和开发的平衡;同时选用黄金正弦算法变异进一步提高解的精度。其次,对12个基准函数进行仿真求解,实验数据表明平均值、标准差、Wilcoxon检验和收敛曲线均优于基本樽海鞘群和其他群体智能算法,证明了所提算法具有较高的寻优精度和收敛速度。最后,将BAGSSA应用于移动机器人路径规划问题中,并在两种测试环境中进行仿真实验,仿真结果表明,改进樽海鞘群算法较其他算法所寻路径更优,并具有一定理论与实际应用价值。
文摘针对基本樽海鞘群算法收敛速度慢、收敛精度低、易陷入局部最优的缺点,提出了一种融合黄金正弦混合变异的自适应樽海鞘群算法AGHSSA(Adaptive Salp Swarm Algorithm with Golden Sine Algorithm and Hybrid Mutation)。该算法引入了自适应变化的权重因子以加强精英个体的引导作用,提升收敛速度与精度。通过黄金正弦算法优化领导者位置更新方式,增强算法的全局搜索和局部开发能力。融合邻域重心反向学习与柯西变异对最优个体位置进行扰动,提升算法跳出局部最优的能力。通过对12个基准测试函数进行仿真实验来评估改进算法的寻优能力,实验结果表明,改进算法能显著提升寻优速度和精度,并且具备较强的跳出局部最优的能力。
