Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.
Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management
M. Janga Reddy, D. Nagesh Kumar; Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management. Journal of Hydroinformatics 1 January 2009; 11 (1): 79–88. doi: https://doi.org/10.2166/hydro.2009.042
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