首页|基于折射反向学习的改进正弦余弦探路者算法

基于折射反向学习的改进正弦余弦探路者算法

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针对探路者算法(Pathfinder Algorithm,PFA)在寻优时收敛速度慢、求解精度低与极易陷入局部最优等问题,提出一种基于折射反向学习的改进正弦余弦探路者算法运用于函数优化问题当中.首先,通过折射反向学习策略初始化种群,利用折射与反向原理相结合使初始解更加靠近最优解位置,优质的种群定位能为迭代期的策略执行提供良好基础;其次,在探路者位置更新阶段引入改进的正弦余弦个体位置更新方式,该方式将原更新式中的线性步长搜索因子进行替换,以非规律的模式产生新代探路者个体,从而降低个体忽略最优解的概率,同时提出一种自适应权重添加至原更新式当中,配合正、余弦函数对算法的全局搜索与局部开发能力进行平衡;最后,将本文算法运用于12个经典的基准测试函数与10个具有复杂特征的CEC2014基准测试函数上进行寻优求解,并将其运用于压力容器设计与三杆桁设计问题,同时选取了合适的评价指标对算法性能进行评估.实验结果表明:本文算法在收敛速度、寻优精度与局部最优规避性方面均有较大提升,出色的工程优化性能也证明了本文算法的鲁棒性.
Improved sine and cosine pathfinder algorithm with refracted opposition-based learning
An improved sine and cosine pathfinder algorithm with refracted opposition-based learning is proposed for the function optimization problem to address the problems of slow convergence,low accuracy of the pathfinder algorithm,and the tendency to fall into local optimality.First,the population is initialized by a refracted opposition-based learning strategy.The combination of refraction and inverse principle is used to make the initial solution closer to the optimal solution position,and the high-quality population positioning can provide a good basis for the strategy execution in the iteration period.Second,an improved sine and cosine individual position update approach is introduced in the pathfinder position update phase,which replaces the linear step search factor in the original update equation to generate new generations of pathfinder individuals in a non-regular pattern,thus reducing the probability of individuals ignoring the optimal solution.An adaptive weight is also proposed to be added to the original update formula to balance the global search and local exploitation ability of the algorithm with the sine and cosine functions.Finally,the proposed algorithm is applied to twelve classical benchmark test functions and ten CEC2014 benchmark test functions with complex features for finding the optimal solution,and it is applied to pressure vessel design and three-rod truss design problems,and suitable evaluation indexes are selected to assess the performance of the algorithm.The experimental results show that the proposed algorithm has improved in terms of convergence speed,optimization-seeking accuracy,and local optimum avoidance,and the excellent engineering optimization performance also demonstrates the robustness of ours.

pathfinder algorithmfunction optimization problemrefracted opposition-based learningsines and cosines algorithmengineering optimization problem

毛雪迪、王冰、夏煌智

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牡丹江师范学院数学科学学院,黑龙江牡丹江 157011

牡丹江师范学院应用数学研究所,黑龙江牡丹江 157011

探路者算法 函数优化问题 折射反向学习 正弦余弦算法 工程优化问题

国家自然科学基金牡丹江师范学院科研项目牡丹江师范学院校级项目牡丹江师范学院校级项目

12271036GP2020003kjcx2022-097mdjnukjcx2022-019mdjnu

2024

10.19304/J.ISSN1000-7180.2023.0249

微电子学与计算机
中国航天科技集团公司第九研究院第七七一研究所

微电子学与计算机

CSTPCD
影响因子：0.431
ISSN：1000-7180
年,卷(期)：2024.41(3)
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