An improved symbiotic organisms search algorithm for higher dimensional optimization problems

摘要

The symbiotic organisms search (SOS) algorithm was introduced by considering the relationships among the creatures in a natural ecosystem. Despite the superior efficiency of SOS, it has been observed that fixing benefit factors of mutualism phase at 1 or 2; the algorithm obstructs itself from an extensive and diverse search of the search region. Moreover, alteration of random dimensions in the parasitism phase increases the computational burden of the algorithm. Considering these limitations, a modified SOS algorithm, namely nwSOS, has been proposed in this study to solve higher dimensional optimization problems. In the suggested nwSOS, the benefit factors are calculated by a non-linear approach. The mutual vector is modified, and weights of both benefit factors are utilized to effectively explore and exploit the search region. Moreover, the parasitism phase is tailored to lessen the computational overhead. The modified method is then used to evaluate twenty basic benchmark functions using 100 and 500 dimensions. Results are compared with six state-of-the-art algorithms and with SOS and its five modified variants. Four designing issues from both unconstrained and constrained classifications are solved utilizing nwSOS. Complexity analysis, statistical analysis, and convergence analysis are executed to measure the algorithm’s effectiveness from different aspects. Moreover, the proposed algorithm has been used for segmenting COVID-19 chest X-ray images with the help of multi-level thresholding approach using different thresholds. All the results confirmed the enhancement of the proposed algorithm.