Crossover Methods Comparison in Flood Evacuation Route Optimization

Abstract

This study aims to implement the genetic algorithm by testing the appropriate crossover methods in order to obtain optimal disaster evacuation routes based three main indicators, namely travel time, possible transportation mode, and affected road conditions. The research phase begins with establishing a flood-affected area scenario consisting of the victim's initial location, evacuation location, routing areas, affected road conditions, distance, as well as travel time. The genetic algorithm is applied by representing the genes and chromosomes based on the available data, generating the initial population and calculating the fitness value. At the stage of determining the parent in forming a new individual, roulette wheel selection is used. For the crossover method to produce new individuals, there are 3 methods tested namely single-point, two-point and uniform crossover. The new formed individuals are then mutated with a probability level of 0.1. The last stage is to form a new population by sorting individuals with the highest fitness value. These processes took place with an iteration limit of 1000. Based on the results of the implementation and tests conducted, the uniform crossover method has the most optimal results with accuracy 90% and highest fitness value of 0.896. Meanwhile, the two others methods two-point and single-point have extremely lower accuracy which are 70% and 60% respectively. This result confirmed the statement of previous research which convinced that the uniform crossover is the most effective crossover method.