Niched Pareto genetic algorithm (NPGA) is a superior method to solve the multi-objective optimization problems because it is applicable to a large range of variable problems, and can search nonlinear and discontinuous space without need for continuity and second-order partial differential operators. However, it is inefficient in finding the Pareto optimal solutions due to two shortcomings:premature convergence to local area and low convergence speed. In this paper, an improved NPGA (INPGA) is developed to promote the solving ability of algorithms. The main improvements of INPGA include three aspects: the Pareto solution set filter, the elite individual preservation strategy and the neighborhood space Muhlenbein mutation. Moreover, the message passing interface (MPI) for parallel computing and the operation library of individual fitness is introduced in the INPGA to improve calculation speed. Also, the INPGA is applied to a two-dimensional hypothetical test problem to demonstrate the multi-objective optimal design of a groundwater pump-and-treat system. The comparison of results shows that the INPGA is superior to the NPGA in finding the tradeoff curve with a range of applicable Pareto optimal solutions.%基于小生境技术的Pareto遗传算法(NPGA)是一种求解多目标问题的智能搜索方法,适用于优化多种非线性、不连续等复杂多目标问题.但该算法存在局部早熟收敛和收敛速度慢两个不足,在求解Pareto前沿上效果不佳.本文在NPGA的基础上,提出了改进NPGA方法(INPGA),通过Pareto解集过滤器、精英个体保留策略、邻域空间Miihlenbein变异等三项改进措施.提高了算法的求解能力.同时,应用个体适应值库操作和MPI(Message Passing Interface)并行计算技术来提高求解速度.最后将该方法应用于一个理想二维地下水污染修复问题的多目标优化求解,结果表明,该算法求解过程简单,计算时间短,优化得到的Pareto解集权衡曲线的跨度更为合理,具有很好的应用效果.
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