论文部分内容阅读
An application of multi-objective particle swarm optimization(MOPSO) algorithm for optimization of the hydrological model(HYMOD) is presented in this paper.MOPSO algorithm is used to find non-dominated solutions with two objectives: high flow Nash-Sutcliffe efficiency and low flow Nash-Sutcliffe efficiency.The two sets’ coverage rate and Pareto front spacing metric are two criterions to analyze the performance of the algorithms.MOPSO algorithm surpasses multi-objective shuffled complex evolution metropolis(MOSCEM_UA) algorithm in terms of the two sets’ coverage rate.But when we come to Pareto front spacing rate,the non-dominated solutions of MOSCEM_UA algorithm are better-distributed than that of MOPSO algorithm when the iteration is set to 40000.In addition,there are obvious conflicts between the two objectives.But a compromise solution can be acquired by adopting the MOPSO algorithm.
An application of multi-objective particle swarm optimization (MOPSO) algorithm for optimization of the hydrological model (HYMOD) is presented in this paper. MOPSO algorithm is used to find non-dominated solutions with two objectives: high flow Nash-Sutcliffe efficiency and low flow Nash-Sutcliffe efficiency. The two sets ’coverage rate and Pareto front spacing metric are two criterions to analyze the performance of the algorithms. MOPSO algorithm surpasses multi-objective shuffled complex evolution metropolis (MOSCEM_UA) algorithm in terms of the two sets’ coverage rate.But when we come to Pareto front spacing rate, the non-dominated solutions of MOSCEM_UA algorithm are better-distributed than that of MOPSO algorithm when the iteration is set to 40000. In addition, there are obvious conflicts between the two objectives.But a compromise solution can be acquired by employing the MOPSO algorithm.