公路线形优化的多目标遗传算法

2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making Pub Date : 2007-04-01 DOI:10.1109/MCDM.2007.369448

M. Jha, A. Maji

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引用次数: 15

摘要

我们开发了一个多目标的方法来优化三维(3D)公路线形使用遗传算法。多目标遗传算法在处理各种目标之间的权衡方面非常流行。帕累托最优的概念已经在著作中被引入，并为此开发了多目标遗传算法。我们发现，每个问题都是独一无二的，没有黑盒方法可以在所有问题中实现多目标遗传算法。我们实现了帕累托最优的概念，开发了一个多目标遗传算法的三维公路线形优化问题，我们已经工作了10年。我们将多目标优化方法应用于我们以前研究过的一个示例问题。结果表明，多目标方法能够在各目标之间求得最佳权衡，从而得到最优解

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A Multi-Objective Genetic Algorithm for Optimizing Highway Alignments

We develop a multi-objective approach to optimize 3-dimensional (3D) highway alignments using a genetic algorithm. Multi-objective genetic algorithms have been very popular for handling trade-offs among various objectives. The concept of Pareto optimally has been introduced in works and multi-objective genetic algorithms have been developed for this purpose. What we have found is that every problem is unique and there is no black box approach to implement multi-objective genetic algorithms in all problems. We implement the Pareto-optimality concept to develop a multi-objective genetic algorithm for the 3D highway alignment optimization problem on which we have worked for the last 10 years. We apply the multi-objective optimization approach to an example problem on which we had previously worked. The results suggest that the multi-objective approach has great promise for obtaining the best trade-off among various objectives to reach an optimal solution

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2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making

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