通过优化候选解更新策略改进的算术优化算法

Improved Arithmetic Optimization Algorithm by Optimizing Candidate Solutions Update Strategy

为解决算术优化算法收敛速度较慢、不易寻找局部最优值的问题,提出一种优化候选解更新策略改进的算术优化算法IAOA。首先,将算术优化算法中每个候选解每一维度值的更新策略改为乘除或加减操作。其次,优化算法候选解的位置更新策略以增加全局勘探能力和候选解多样性,使算法寻找更优值。经过21个基准函数的仿真实验表明,相较于樽海鞘群算法、灰狼优化算法、阿基米德优化算法和算术优化算法,IAOA算法在11个基准函数中取得最优值,并在多数基准函数上具有更快的收敛速度,可为解决算术优化算法提供新的思路。

In order to solve the problem of slow convergence speed of arithmetic optimization algorithm and difficulty in finding local optimal value,an improved arithmetic optimization algorithm IAOA with updating strategy of optimization candidate solution is proposed.First,the update strategy of each dimension value of each candidate solution in the arithmetic optimization algorithm is changed to multiplication and division or addition and subtraction operations.Secondly,the position update strategy of the algorithm candidate solution is optimized to increase the global exploration capability and candidate solution diversity,so that the algorithm can find a better value.The simulation experiment of 21 benchmark functions shows that IAOA algorithm achieves the optimal value in 11 benchmark functions,and has faster convergence speed in most benchmark functions,compared with the Tartar sea squirt group algorithm,grey wolf optimization algorithm,Archimedes optimization algorithm and arithmetic optimization algorithm,in order to provide new ideas for solving arithmetic optimization algorithm.