shuffled frog leaping algorithm

The optimal design and construction of sewage networks have always been considered by researchers and experts due to the very high costs of implementing this infrastructure. Being consisted of various variables and subjected to complex constraints, conventional mathematical optimization procedures are unlikely to be able to solve sewage network optimization problems. Thus, utilizing meta-heuristic optimization algorithms is a must to tackle these problems. The shuffled frog leaping algorithm (SFLA) is one of the new meta-heuristic algorithms which has shown its ability to solve a large number of optimization problems. In this research, the capability of the SFLA in solving the problem of optimal design of sewage networks has been investigated. The diameter of the pipes as discrete decision variables and the depth of pipe placement as continuous decision variables were simultaneously considered in this study as the unknowns. To this end, three sewage networks with 13, 41, and 65 decision variables have been selected as case studies. Various technical, operational, and hydraulic constraints are controlled by defining appropriate penalty functions. The results showed that for case studies 1 and 3, the SFLA decreased the minimum construction costs derived by GA, PSO, and SCE algorithms by 0.43 and 3.2 percent respectively, and for the second case study, with the less number of function evaluations, SFLA has reached the equal objective function compared to other algorithms.