Optimization to optimization (OtoO) approach is proposed in this study. It aims to increase an optimization algorithm performance. OtoO approach has two types of optimization methods. First is essential algorithm, which is used for solution of the basic problem. Second is auxiliary algorithm that adjusted the parameters of the essential algorithm. In this study, the monarchy butterfly optimization (MBO) method and stochastic multi-parameter divergence optimization (SMDO) method were defined as essential algorithm and auxiliary algorithm, respectively. Constant parameters of the MBO method that affect performance (Keep, Max. Step Size, period and BAR) are primarily optimized on benchmark functions with the SMDO algorithm, and results are compared with each other and classical MBO, ABC (Artificial Bee Colony), ACO (Ant Colony), BBO (Biogeography-based), SGA (Simple Genetic) and DE (Differential Evolution) algorithms. In addition, OtoO approach is also tried via composite benchmark functions. In addition, PI and PID controllers were designed for the load frequency control of a hybrid power system. Results are compared with the FA (Firefly Algorithm) and GA (Genetic Algorithm) results. Results demonstrate that the performance of algorithms can be increased without disrupting the basic philosophy of algorithms and hybridizing algorithms with the proposed OtoO approach via benchmark functions and engineering problems.

本研究提出了从优化到优化（OtoO）的方法。其目的是提高优化算法的性能。OtoO方法有两种类型的优化方法。首先是基本算法，用于解决基本问题。其次是辅助算法，它调整了基本算法的参数。在这项研究中，君主蝶型优化（MBO）方法和随机多参数发散优化（SMDO）方法分别定义为基本算法和辅助算法。会影响性能的MBO方法的常数参数（保持，最大步长，周期和BAR）主要是使用SMDO算法在基准函数上进行了优化，并将结果与​​经典MBO，ABC（人工蜂群）进行了比较， ACO（蚁群）BBO（基于生物地理），SGA（简单遗传）和DE（差异进化）算法。此外，还可以通过复合基准测试功能尝试OtoO方法。此外，PI和PID控制器设计用于混合动力系统的负载频率控制。将结果与FA（萤火虫算法）和GA（遗传算法）结果进行比较。结果表明，可以在不破坏算法基本原理的情况下提高算法性能，并通过基准功能和工程问题将算法与提出的OtoO方法混合。将结果与FA（萤火虫算法）和GA（遗传算法）结果进行比较。结果表明，可以在不破坏算法基本原理的情况下提高算法性能，并通过基准功能和工程问题将算法与提出的OtoO方法混合。将结果与FA（萤火虫算法）和GA（遗传算法）结果进行比较。结果表明，可以在不破坏算法基本原理的情况下提高算法性能，并通过基准功能和工程问题将算法与提出的OtoO方法混合。