自适应分组融合改进算数优化算法及应用

Adaptive Grouping Fusion Improved Arithmetic Optimization Algorithm and Its Application

针对算数优化算法(Arithmetic Optimization Algorithm, AOA)寻优速度慢、精度低和易受局部极值点影响的问题,提出了一种自适应分组融合改进算数优化算法(Adaptive Grouping Fusion Improved Arithmetic Optimization Algorithm, AG-AOA)。首先,采用Halton序列初始化个体位置,提高迭代初期算法的多样性;然后,引入自适应分组策略对种群进行分组操作,根据适应度值大小把个体自适应分为优势组、均势组和劣势组;最后,对各组个体分别采用教与学优化策略、精英反向学习策略和振荡扰动算子进行位置更新,以提高AOA的搜索能力,减小局部极值点对算法的影响。通过包含各种复杂程度的测试函数对AG-AOA的性能进行验证,包括基准测试函数、统计显著性的Wilcoxon秩和检验以及部分CEC2014测试函数。将AG-AOA应用于两个实际工程优化问题,并将所得结果与其他元启发式算法进行了比较和分析,验证了AG-AOA的优越性。

The arithmetic optimization algorithm(AOA) has slow convergence speed and low convergence accuracy, and is easy to fall into local extremum.In order to solve these problems, an adaptive grouping fusion improved arithmetic optimization algorithm(AG-AOA) is proposed.Firstly, Halton sequence is used to initialize individual positions to improve the diversity of algorithm at the initial iteration stage.Then, an adaptive grouping strategy is introduced to group the population, and the adaptive individuals are divided into dominant group, equilibrium group and inferior group according to the fitness value.Finally, the teaching and learning optimization strategy, elite reverse learning strategy and oscillating disturbance operator are used to update the position of each group of individuals to improve the searching ability of AOA and reduce the influence of local extreme points on the algorithm.The performance of AG-AOA is validated using test suites containing problems of wide varieties of complexities.Various analyses are conducted, including benchmark function, Wilcoxon ranksum test for statistical significance and part of CEC2014 test function.Finally, AG-AOA is applied to two practical engineering optimization problems, the obtained results are then analysed and compared and with other metaheuristics algorithms to show the superiority of the proposed AG-AOA.