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Corridor planning problems are challenging because their solution often requires the participation of multiple stakeholders with different interests and emphases. Though such problems fall into the domain of multiobjective evaluation, existing corridor location models often search for a single global optimum by collapsing multiple objectives into a single one using a weighting method. In multiobjective problems with competing objectives, however, optimality will often have different interpretations among decision makers, and, as a consequence, no single optimal solution will satisfy all participants. This paper describes the design and implementation of a multiobjective genetic algorithm for corridor selection problems (MOGADOR). This new approach generates a large set of Pareto-optimal and near-optimal solutions that can be evaluated with respect to the untargeted or imprecisely modeled characteristics of ill-structured corridor location problems. Experimental results suggest that the MOGADOR approach outperforms traditional shortest-path methods in both computation time and solution quality. An analytical and visualization tool is provided to help decision makers identify good candidates and evaluate trade-offs among alternatives.

Multiobjective site-search problems are a class of decision problems that have geographical components and multiple, often conflicting, objectives; this kind of problem is often encountered and is technically difficult to solve. In this paper we describe an evolutionary algorithm (EA) based approach that can be used to address such problems. We first describe the general design of EAs that can be used to generate alternatives that are optimal or close to optimal with respect to multiple criteria. Then we define the problem addressed in this research and discuss how the EA was designed to solve it. In this procedure, called MOEA/Site, a solution (that is, a site) is encoded by using a graph representation that is operated on by a set of specifically designed evolutionary operations. This approach is applied to five different types of cost surfaces and the results are compared with 10 000 randomly generated solutions. The results demonstrate the robustness and effectiveness of this EA-based approach to geographical analysis and multiobjective decisionmaking. Critical issues regarding the representation of spatial solutions and associated evolutionary operations are also discussed.