Improve the efficiency of salp swarm algorithm for sewer system network design optimization problems

Sewer network construction is a significant investment, and designing a cost-effective network that meets minimum constraint requirements is a complex challenge. This study aims to find an optimal cost solution to reduce investment capital while ensuring efficient operation and high performance of the sewer network. By optimizing technical parameters that directly affect construction costs, the research introduces a novel hybrid algorithm based on the salp swarm algorithm (SSA) and the dragonfly algorithm (DA). This SSA-DA hybrid algorithm addresses the limitations of the individual SSA and DA by effectively balancing exploration and exploitation processes. The feasibility and effectiveness of the proposed algorithm were validated through two case studies from previous research on sewer networks. Quantitative analysis showed that the SSA-DA algorithm consistently outperformed other methods, including SSA and DA alone, achieving lower construction costs. Qualitative analysis confirmed the algorithm's reliability, as evidenced by consistent results across 10 independent runs. This research significantly contributes to the field by providing a robust and efficient optimization tool for cost-effective sewer network design.