Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation

A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist-mutation (EM) operator are introduced. The proposed EM-MOPSO approach is first tested for few test problems taken from the literature and evaluated with standard performance measures. It is found that the EM-MOPSO yields efficient solutions in terms of giving a wide spread of solutions with good convergence to true Pareto optimal solutions. On achieving good results for test cases, the approach was applied to a case study of multi-objective reservoir operation problem, namely the Bhadra reservoir system in India. The solutions of EM-MOPSOs yield a trade-off curve/surface, identifying a set of alternatives that define optimal solutions to the problem. Finally, to facilitate easy implementation for the reservoir operator, a simple but effective decision-making approach was presented. The results obtained show that the proposed approach is a viable alternative to solve multi-objective water resources and hydrology problems. Copyright (c) 2007 John Wiley & Sons, Ltd.