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Enhanced symbiotic organisms search (ESOS) for global numerical optimization

机译:增强共生生物搜索(eSOS),用于全局数值优化

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Symbiotic organisms search (SOS) is a simple yet effective metaheuristic algorithm to solve a wide variety of optimization problems. Many studies have been carried out to improve the performance of the SOS algorithm. This research proposes an improved version of the SOS algorithm called the “enhanced symbiotic organisms search” (ESOS) for global numerical optimization. The conventional SOS is modified by implementing a new searching formula into the parasitism phase to produce a better searching capability. The performance of the ESOS is verified using 26 benchmark functions and one structural engineering design problem. The results are then compared with existing metaheuristic optimization methods. The obtained results show that the ESOS gives a competitive and effective performance for global numerical optimization.
机译:共生生物搜索(SOS)是一种简单而有效的成群质算法,可以解决各种优化问题。已经进行了许多研究以提高SOS算法的性能。本研究提出了一种改进的SOS算法版本,称为“增强共生生物搜索”(ESO)进行全局数值优化。通过将新的搜索公式实施到寄生期阶段来产生更好的搜索能力来修改传统的SOS。使用26个基准功能和一个结构工程设计问题来验证ESOS的性能。然后将结果与现有的成群质优化方法进行比较。所获得的结果表明,ESO给出了全球数值优化的竞争性和有效性能。

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