Application of evolutionary computing on least-cost water distribution network designs

The optimization of water distribution networks are complex, multi-modal and discrete-variable problems that cannot be easily solved with conventional optimization algorithms. This study adopts ant algorithm (AA), immune algorithm (IA), and scatter search (SS) which are all evolutionary computing techniques to avoid the entrapments by local optimal solutions, to obtain the least-cost designs of water distribution networks. One benchmark water distribution network optimization problems is used as a case study. Comparisons of the results of this study with relevant literature data indicate that AA and SS are able to find solutions better than those provided by some of the most competitive algorithms published in the literature. Furthermore, the results of the success rate evaluation indicate that AA can 100% successfully achieve the global optimum.