3D flight planning optimisation using genetic algorithm - Hebert. (2013)
Genetic Algorithm (GA) has been extensively used for optimisation problems especially for flight planning problems. This report elaborates about the 3D problems undergoing the GA to solve for optimised solution. A 4-unit cube containing 125 nodes is visualised as a 3D model. Initialisation population of 50 individuals is generated in various numbers of generations such as 30, 60 and 100 generations to obtain the best route (the least total distance route) travelling from departure node to destination node. Roulette-wheel selection is used to select the parents that subsequently undergo the crossover and mutation at the rate inputted by the user. Then, the resulted new offspring will replace the parents forming a new population. After generating different numbers of generations, the results show that the average total distance in a population decreases over generations. Hence, the overall fitness of the population is better from generation to generation. These results also prove that there is a room for developing 3D flight planning problems using the genetic algorithm in future studies.