Multiobjective evolutionary path planning via fuzzy tournament selection

The paper introduces a new selection algorithm that can be used for evolutionary path planning systems. This new selection algorithm combines fuzzy inference along with tournament selection to select candidate paths (CPs) to be parents based on: (1) the Euclidean distance from origin to destination, (2) the sum of the changes in the slope of a path, and (3) the average change in the slope of a path. The authors provide a detailed description of the fuzzy inference system used in the new fuzzy tournament selection algorithm (FTSA) as well as some examples of its usefulness. They use 12 instances of the FTSA to rank a population of CPs using the above criteria. Based on its path ranking capability, they show how the FTSA can obviate the need for the development of an explicit multiobjective evaluation function. Finally, they use the FTSA to enhance the performance of an existing evolutionary path planning system called GEPOA.

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