An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit currently available resources. One these is ensuring that reliable and near-optimum designs distribution systems (WDSs) are achieved. Designing WDSs a discrete combinatorial NP-hard optimization problem, its complexity increases when more objectives added. Among many existing evolutionary algorithms, new hybrid fast-convergent multi-objective particle swarm (MOPSO) algorithm developed increase convergence diversity rates resulted non-dominated solutions in terms network capital cost reliability using minimized computational budget. Several introduced algorithm, which self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, hypervolume quality for selecting repository members. A local search method also coupled both original MOPSO newly one. Both algorithms applied medium large benchmark problems. results with superior different performance metrics medium-sized network. In contrast, without performed better