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In this study, Modified Monarch Butterfly Optimization Algorithm (M2BO) is proposed by modeling stochastic processes in Monarch Butterfly Optimization (MBO) algorithm with different random distribution functions. The proposed M2BO algorithm has been firstly tested with benchmark functions and the results have been compared with the literature and classical MBO algorithm. In order to analyze the performance of the proposed M2BO algorithm for the real engineering problem, the feedback gain matrix (K matrix) for the control of the 3 Degree of Freedom (3 DOF) Hover system has been optimized. The results have been compared with classical MBO, DSO (Discrete Stochastic Optimization), and SMDO (Stochastic Multi-parameter Divergence Optimization) optimization algorithms. The obtained results have been compared on 3 DOF Hover simulation models and real-time 3 DOF Hover experimental sets. The performance of the proposed M2BO algorithm in benchmark and real engineering problem tests has been shown theoretically and experimentally. Thus, it has been shown that the performance of algorithms can be increased without changing the basic philosophy of algorithms by modeling stochastic processes in algorithms with random distributions other than a uniform distribution. In addition to these, it has been determined that distribution function-based contributions can be applied to many of these algorithms. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. |
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