多策略融合算术优化算法及其工程优化

针对算术优化算法(AOA)在搜索过程中容易陷入局部极值点、收敛速度慢以及求解精度低等缺陷,提出一种多策略集成的算术优化算法(MFAOA)。首先,采用Sobol序列初始化AOA种群,增加初始个体的多样性,为算法全局寻优奠定基础;然后,重构数学优化器加速函数(MOA),权衡全局搜索与局部开发过程的比重;最后,利用混沌精英突变策略,改善算法过于依赖当前最优解的问题,增强算法跳出局部极值的能力。选用12个基准函数和部分CEC2014测试函数进行实验仿真,结果表明MFAOA在求解精度和收敛速度上均有明显的提升;另外,通过对两个工程实例进行优化,验证了MFAOA在工程优化问题上的可行性。

Multi-strategy fusion arithmetic optimization algorithm and its application of project optimization

Aiming at the shortcomings of arithmetic optimization algorithm(AOA) in the search process, such as local extreme points, slow convergence speed and low solution accuracy, this paper proposed a multi-strategy fusion arithmetic optimization algorithm(MFAOA). Firstly, the algorithm used the Sobol sequence to initialize the AOA population, which increased the diversity of the initial individuals and laid the foundation for the global optimization of the algorithm. Then, it reconstructed the mathematical optimizer acceleration function(MOA) to weigh the proportion of the global search and the local development process. Finally, it used the chaotic elite mutation strategy to improve the algorithm’s over-dependence on the current optimal solution and enhanced the algorithm’s ability to jump out of the local extremum. The paper used 12 general benchmark functions and part of CEC2014 test functions for experimental simulation, and the results show that MFAOA has a significant improvement in solution accuracy and convergence speed. Besides, the algorithm introduced two engineering examples for optimization, which verifies the feasibility of MFAOA in engineering optimization problems.