Auto-enhanced population diversity and ranking selection-based differential evolutionary algorithm applied to the optimal design of water distribution system

The differential evolution (DE) algorithm is considered the most powerful evolutionary algorithm (EA) for the optimal design of water distribution systems (WDSs). However, when dealing with large-scale WDS optimization, issues such as premature convergence become a concern. This paper presents an auto-enhanced population diversity and ranking selection-based differential evolutionary (AEPD-RSDE) algorithm for the optimal design of WDSs, which is the first work that incorporates an AEPD strategy to avoid the premature convergence issue and enhance the exploration ability of DE applied to WDS optimization. Besides, the proposed algorithm includes a ranking selection strategy that replaces the tournament selection operator to enhance convergence speed. Three well-known WDSs, i.e., the New York Tunnels (NYT), the Hanoi network (HAN), and the Balerma irrigation network (BIN), were used to validate the proposed algorithm. Results indicate the proposed algorithm is able to find the current best solution, with a success rate of 100% for the NYT and HAN cases and lower average cost solution of €1.921 million for the BIN case relative to other EAs. Instead of solely focusing on ultimate performance comparison, search behavior analyses are conducted between different mutation and selection operators, offering deep insight to guide the development of more advanced EAs. HIGHLIGHTS This paper introduces an auto-enhanced population diversity strategy to overcome premature convergence and enhance the exploration ability of differential evolution (DE) in the optimization of water distribution systems (WDS).; The paper compares the searching behavior of mutation and selection operators and provides an explanation for why the proposed algorithm is more effective than traditional DE.