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针对公路路网的养护需求矛盾,在综合考虑养护资金、投资效益和养护质量等必要目标的基础上,提出采用带精英策略的非支配排序遗传算法(NSGA-Ⅱ)结合惩罚函数算法对养护方案进行全局优化,使得养护决策合理化、科学化。仿真结果表明:在对带约束条件的多目标养护决策优化中,改进的NSGA-Ⅱ算法通过构造加以惩罚函数的适应度函数在解空间进行搜索,能够快速地找出全局最优养护解集,为养护决策提供准确的数值依据,与数学规划和遗传算法比对验证了该算法的有效性和优越性。
Aiming at the contradiction of maintenance requirements of highway network, based on the necessary goals of conservation funds, investment benefits and quality of maintenance, a non-dominated ranking genetic algorithm with elitist strategy (NSGA-Ⅱ) and penalty function algorithm Global optimization, making conservation decision-making rationalization and scientific. The simulation results show that in the optimization of multi-objective conservation decision with constraints, the improved NSGA-Ⅱ algorithm searches the solution space by constructing the fitness function to be penalized function, which can quickly find the global optimal conservation solution set, To provide an accurate numerical basis for conservation decision-making, and compared with mathematical programming and genetic algorithm to verify the effectiveness and superiority of the algorithm.