Algoritmo evolutivo generador de rutas eficientes para el transporte público

Abstract

The primary objective of this study was to implement a computational prototype generator for urban transport routes, inspired by the theory of evolution, to solve the problem of complex combinatorial search in a public transport network. The prototype employed the Python programming language for coding and the metaheuristics known as genetic algorithm (GA). The results showed that the computational prototype was accurate and fast, providing highly efficient solutions for each execution in the urban transport network. The prototype was validated against Mandl's Swiss Road network and achieved better solutions than those presented by previous studies. In conclusion, the evolutionary process of the computational prototype developed here converges to highly satisfactory results due to the innovative mechanisms of genetic crossover, mutation, and quality function operators that equally weight the parameters that comprise it. (English) [ABSTRACT FROM AUTHOR]