For last two decades, nature-inspired metaheuristic algorithms together with their modified, improved, and hybrid versions have been gaining huge popularity in the field of optimization in solving continuous and complex real-life optimization problems. In this work, a novel improved symbiosis organism search (SOS) algorithm, called self-adaptive beneficial factor-based improved SOS (SaISOS, in short) is suggested. The self-adaptive benefit factors and a modified mutualism phase (called “Three-way mutualism phase”) have been introduced here to upgrade the performance of SOS algorithm. A random weighted reflection coefficient and a new control operator have also been introduced. To validate the proposed algorithm and to compare its performance with other state-of-the-art algorithms, 15 IEEE-CEC 2015 functions have been employed and the experimental results confirm that SaISOS provides competitive results on most occasions. Also, the proposed algorithm is used to solve five real-world optimization problems. Considering the average output, it is observed that the proposed method performs significantly better in solving the real-world problems compared to the alternative state-of-the art techniques considered in this work.

中文翻译：

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一种新的改进的共生生物搜索算法

在过去的二十年里，受自然启发的元启发式算法及其修改、改进和混合版本在解决连续和复杂的现实优化问题的优化领域中获得了巨大的普及。在这项工作中，提出了一种新的改进的共生生物搜索（SOS）算法，称为基于自适应有益因子的改进SOS（简称SaISOS）。这里引入了自适应收益因子和改进的互惠阶段（称为“三向互惠阶段”），以提升 SOS 算法的性能。还引入了随机加权反射系数和新的控制算子。为了验证所提出的算法并将其性能与其他最先进的算法进行比较，已使用 15 个 IEEE-CEC 2015 函数，实验结果证实 SaISOS 在大多数情况下都提供有竞争力的结果。此外，所提出的算法用于解决五个现实世界的优化问题。考虑到平均输出，可以观察到，与本工作中考虑的替代最先进技术相比，所提出的方法在解决实际问题方面的表现要好得多。