Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

摘要

Optimal allocation of water resources for various stakeholders often involves considerablencomplexity with several conflicting goals, which often leads to multi-objective optimization.nIn aid of effective decision-making to the water managers, apart from developing effective multi-nobjective mathematical models, there is a greater necessity of providing efficient Pareto optimalnsolutions to the real world problems. This study proposes a swarm-intelligence-based multi-nobjective technique, namely the elitist-mutated multi-objective particle swarm optimizationntechnique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objectivenwater resource management problems. The EM-MOPSO technique is applied to a case study ofnthe multi-objective reservoir operation problem. The model performance is evaluated byncomparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it isnfound that the EM-MOPSO method results in better performance. The developed method can benused as an effective aid for multi-objective decision-making in integrated water resourcenmanagement.