Abstract:
The design of a water distribution network (wdn) is an ever-challenging problem. The formulation and application of optimization techniques for wdn design have been an important area of research. Recently, the introduction of chaos theory-based evolutionary algorithms (eas), in addition to traditional random-based ones, has elevated the scope for further improving the performance of eas. The present study proposes a chaos-directed genetic algorithm (cdga) by incorporating chaos ergodicity in ga mechanics for the optimal design of wdns by introducing two novel frameworks, namely non-sequential approach and sequential approach. In improving the search efficacy of ga, the influence of chaotic systems with high-dimensionality maps is also explored when compared to the low-dimensionality maps. Considering four widely studied wdn benchmark problems ranging from 8 to 454 dimensions, the performance of the proposed ga and cdga models is evaluated. The results show that the cdga models outperform ga with better search efficacy, requiring fewer function evaluations to locate the optimal solution. In addition, the cdga models are found to outperform other optimization techniques reported previously to handle these benchmark problems. Based on the results obtained, the study suggests the use of the chaotic system with other bio-inspired techniques to further improve their searchability and, thus, their computational efficiency. © 2022, the author(s), under exclusive licence to springer-verlag gmbh germany, part of springer nature.