Adegboye Oluwatayomi Rereloluwa, Feda Afi Kekeli, Agyekum Ephraim Bonah, Mbasso Wulfran Fendzi, Kamel Salah
Engineering Management Department, University of Mediterranean Karpasia, Mersin-10, Turkey.
Advanced Research Centre, European University of Lefke, Mersin-10, Turkey.
Heliyon. 2024 May 22;10(11):e31766. doi: 10.1016/j.heliyon.2024.e31766. eCollection 2024 Jun 15.
This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic perturbation and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance the convergence accuracy and speed of the optimization algorithm. The study assesses the SCMSSA algorithm's performance against other optimization algorithms using six test functions to show the efficacy of the enhancement strategies. Furthermore, its efficacy in improving Support Vector Regression (SVR) models for CO prediction is assessed. The results reveal that the SVR-SCMSSA hybrid model surpasses other hybrid models and standard SVR in terms of training and prediction accuracy by obtaining 95 % accuracy. Its swift convergence, precision, and resistance to local optima position make it an excellent choice for addressing complex problems such as CO prediction, with critical implications for sustainability efforts. Moreover, feature importance analysis by SVR-SCMSSA offers valuable insights into the key contributors to CO prediction in the dataset, emphasizing the significance and impact of factors such as fossil fuel, Biomass, and Wood as major contributors to CO emission. The research suggests the adoption of the SVR-SCMSSA hybrid model for more accurate and reliable CO prediction to researchers and policymakers, which is essential for environmental sustainability and climate change mitigation.
本研究提出了一种基于正弦余弦扰动增强、混沌扰动和镜像成像策略的改进盐沼算法（SCMSSA），该算法融合了三种改进机制，以提高优化算法的收敛精度和速度。该研究使用六个测试函数评估了SCMSSA算法相对于其他优化算法的性能，以展示增强策略的有效性。此外，还评估了其在改进一氧化碳（CO）预测的支持向量回归（SVR）模型方面的有效性。结果表明，SVR-SCMSSA混合模型在训练和预测精度方面超过了其他混合模型和标准SVR，准确率达到95%。其快速收敛、高精度以及对局部最优位置的抗性使其成为解决诸如CO预测等复杂问题的理想选择，对可持续发展工作具有重要意义。此外，SVR-SCMSSA的特征重要性分析为数据集中CO预测的关键因素提供了有价值的见解，强调了化石燃料、生物质和木材等因素作为CO排放主要贡献者的重要性和影响。该研究向研究人员和政策制定者建议采用SVR-SCMSSA混合模型进行更准确可靠的CO预测，这对环境可持续性和缓解气候变化至关重要。
