A memetic level-based learning swarm optimizer for large-scale water distribution network optimization

Abstract

Potable water distribution networks are requisites of modern cities. Because of the city expansion, nowadays, the scale of the network grows rapidly, which brings great difficulty to its optimization. Evolutionary computation methods have been widely investigated on small-scale networks, but their performance is far from satisfactory on large-scale networks. Aimed at addressing this difficulty, a new memetic algorithm called level-based learning swarm optimizer with restart and local search is proposed in this paper to solve the large-scale water distribution network optimization problem. Instead of using traditional evolutionary computation algorithms, the level-based learning swarm optimizer that is especially proposed for large-scale optimization problems is applied as the population-based optimizer. Two restart strategies are incorporated to make the algorithm more effective. They can help the algorithm jump out from local optima thus to increase its exploration ability. Moreover, a simple yet effective local search algorithm is proposed based on the domain knowledge to further refine the solutions after the algorithm converges. Experimental results on both single-source and multi-source large-scale water distribution networks show that the proposed algorithm is more effective than the state-of-the-art evolutionary computation algorithms.