Chaos-directed genetic algorithms for water distribution network design: an enhanced search method

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