This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a mul...This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.展开更多
针对捕鱼优化算法(catch fish optimization algorithm,CFOA)容易陷入局部最优、迭代后期种群多样性单一等问题,提出一种多策略融合改进的捕鱼优化算法。首先,通过反向学习策略进行种群初始化,以提高初始种群的质量;其次,引入组长趋同...针对捕鱼优化算法(catch fish optimization algorithm,CFOA)容易陷入局部最优、迭代后期种群多样性单一等问题,提出一种多策略融合改进的捕鱼优化算法。首先,通过反向学习策略进行种群初始化,以提高初始种群的质量;其次,引入组长趋同自适应组队策略,强化算法优势经验的学习;最后,通过引入Lévy飞行螺旋搜索策略,改善集体捕获阶段算法跳出局部最优值的能力;改进算法与灰狼优化(grey wolf optimization,GWO)算法、麻雀优化算法(sparrow search algorithm,SSA)、鲸鱼优化算法(whale optimization algorithm,WOA)、正弦余弦优化算法(sine cosine algorithm,SCA)等7种算法在15个基准测试函数上进行了仿真对比分析。试验结果表明,改进算法在求解精度和收敛速度等方面有较好提升。此外,3个工程设计优化问题的仿真试验进一步验证了改进算法在处理工程优化问题上的优越性。展开更多
文摘This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.
