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首页> 外文期刊>Journal of Hydroinformatics >Performance Evaluation Of Elitist-mutated Multi-objective Particle Swarm Optimization For Integrated Water Resources Management
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Performance Evaluation Of Elitist-mutated Multi-objective Particle Swarm Optimization For Integrated Water Resources Management

机译：水资源综合管理的突变突变多目标粒子群算法性能评估

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摘要

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.
机译：为各个利益相关者优化水资源配置往往会涉及相当复杂的目标，并且存在多个相互矛盾的目标，这通常会导致多目标优化。除了开发有效的多目标数学模型外，为水管理者做出有效的决策，更需要为实际问题提供有效的帕累托最优解。这项研究提出了一种基于群体智能的多目标技术，即精英变异多目标粒子群优化技术（EM-MOPSO），以实现针对多目标水资源管理问题的有效帕累托最优解。 EM-MOPSO技术应用于多目标水库调度问题的案例研究。通过与非支配排序遗传算法（NSGA-Ⅱ）模型的结果进行比较，评估了模型的性能，发现EM-MOPSO方法具有更好的性能。所开发的方法可以作为水资源综合管理中多目标决策的有效辅助手段。

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