Application of Pseudo-Parallel Genetic Algorithm in Optimal Operation of Multisource Water Supply Network

Based on a hydraulic simulation model, the operational optimization problem for the multi-source water supply network is proposed. The objective is to minimize the operation costs in the water distribution system including pumping cost and cost for water purification. In the case of a large-scale water network with many pump stations and tanks, there will be a huge computational load when a conventional genetic algorithm is used to solve the optimization problem. Therefore, an improved genetic algorithm is presented in this paper. Firstly, a mixed-coding methodology is adopted according to the characteristics of the decision variables. For fixed speed pumps, use 1 or 0 representing open or close operation; while for variable speed pumps, use a hybrid code to denote the running states. Secondly, a Pseudo-Parallel GA (PPGA) is designed and implemented, which is based on a coarse grained scheme. In this PPGA, several populations are firstly, generated and evolve separately, and the migration of individuals among the populations is performed through an exchanging tunnel in which the best individuals from different populations can be exchanged with each other. The PPGA is implemented on a PC using multithread programming techniques. The PPGA can not reduce the computer time obviously because the work is still performed on single personal computer rather than in a real parallel computing environment. However, the evolution process of those populations may be accelerated through the migration of individuals. Both SGA and PPGA were applied to solve an operational optimization problem for a case study water network. Compared to SGA, the PPGA is proved to be more efficient and able to find much better solutions. The results also show that substantial saving on operation costs could be achieved if the optimal solution was adopted. This paper was presented at the 8th Annual Water Distribution Systems Analysis Symposium which was held with the generous support of Awwa Research Foundation (AwwaRF).