Improved Genetic Algorithm for Aircraft Departure Sequencing Problem

Abstract

Optimization model is build for solving the aircraft departure sequencing problem in this paper first. Then, an improved genetic algorithm (GA) using symbolic coding is proposed, where a type of total probability crossover and big probability mutation are performed. In this way, the evolutionary policy of Particle Swarm Optimization (PSO) is absorbed into the improved GA, which reduces the complexity and enhance the efficiency greatly. Last, a simulation program using basic GA, adaptive GA, and improved GA is performed. The simulation result shows that the model is effective and Improved GA has better performance than Basic GA or Adaptive GA.