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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A new hybrid differential evolution algorithm with self-adaptation for function optimization
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A new hybrid differential evolution algorithm with self-adaptation for function optimization

机译：一种新的功能优化自适应混合差分演化算法

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摘要

In this study, a new hybrid algorithm, hDEBSA, is proposed with the aid of two evolutionary algorithms, Differential Evolution (DE) and Backtracking Search Optimization Algorithm (BSA). The control parameters of both algorithms are simultaneously considered as a self-adaptation basis such that the values of the parameters update automatically during the optimization process to improve performance and convergence speed. To validate the proposed algorithm, twenty-eight CEC2013 test functions are considered. The performance results of hDEBSA are validated by comparing them with several state-of-the-art algorithms that are available in literature. Finally, hDEBSA is applied to solve four real-world optimization problems, and the results are compared with the other algorithms, where it was found that the hDEBSA performance is better than that of the other algorithms.
机译：在该研究中，借助于两个进化算法，差分演进（DE）和回溯搜索优化算法（BSA），提出了一种新的混合算法HDEBSA。 两种算法的控制参数同时被认为是自适应的基础，使得参数在优化过程中自动更新的值，以提高性能和收敛速度。 为了验证所提出的算法，考虑了二十八个CEC2013测试功能。 通过将它们与文献中可用的几种最先进的算法进行比较来验证HDEBSA的性能结果。 最后，HDEBSA用于解决四个真实世界优化问题，结果与其他算法进行比较，发现HDEBSA性能优于其他算法。

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