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The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework

机译：以最小化外部性为目标的多目标网络设计问题：遗传算法与模拟退火框架的比较

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Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types which are associated with certain link characteristics are the discrete decision variables. Two distinct solution approaches for this multi-objective optimization problem are compared. The first heuristic is the non-dominated sorting genetic algorithm II (NSGA-II) and the second heuristic is the dominance based multi objective simulated annealing (DBMO-SA). Both heuristics have been applied on a small hypothetical test network as well as a realistic case of the city of Almelo in the Netherlands. The results show that both heuristics are capable of solving the MO NDP. However, the NSGA-II outperforms DBMO-SA, because it is more efficient in finding more non-dominated optimal solutions within the same computation time and maximum number of assessed solutions.
机译：将外部性纳入多目标网络设计问题（MO NDP）作为目标是设计可持续网络的重要一步。在本研究中，该问题被定义为双层优化问题，其中以最小化外部性为目标，而与某些链接特征相关的链接类型为离散决策变量。比较了针对此多目标优化问题的两种不同的解决方法。第一种启发式方法是非支配排序遗传算法II（NSGA-II），第二种启发式方法是基于优势的多目标模拟退火（DBMO-SA）。两种启发式方法均已应用于小型假设测试网络以及荷兰阿尔默洛市的实际案例中。结果表明，两种启发式方法都能够解决MO NDP问题。但是，NSGA-II的性能优于DBMO-SA，因为它在相同的计算时间内和最大数量的评估解决方案中查找更多非支配性最优解的效率更高。

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