Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Information Sciences: an International Journal
Dynamic population variation in genetic programming
Information Sciences: an International Journal
Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Editorial: Modelling uncertainty
Information Sciences: an International Journal
Information Sciences: an International Journal
Particle swarm optimisation of interplanetary trajectories from Earth to Jupiter and Saturn
Engineering Applications of Artificial Intelligence
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The aim of this paper is to study the use of a genetic algorithm (GA) to optimise the ascent trajectory of a conventional two-stage launcher. The equations of motion of this system lack analytical solutions, and the number of adjustable parameters is large enough that the use of some non-traditional optimisation method becomes necessary. Two different missions are considered: first, to reach the highest possible stable, circular Low Earth Orbit (LEO); and second, to maximise the speed of a tangential escape trajectory. In this study, three variables are tuned and optimised by the GA in order to satisfy mission constraints while maximising the target function. The technical characteristics and limitations of the launcher are taken into account in the mission model, and a fixed payload weight is assumed. A variable mutation rate helps expand the search area whenever the population of solutions becomes uniform, and is shown to accelerate convergence of the GA in both cases. The obtained results are in agreement with technical specifications and solutions obtained in the past.