A hybrid evolutionary algorithm which synergistically exploits differential evolution, genetic algorithms and particle swarm optimization, has been developed and applied to spacecraft trajectory optimization. The cooperative procedure runs the three basic algorithms in parallel, while letting the best individuals migrate to the other populations at prescribed intervals. Rendezvous problems and round-trip Earth-Mars missions have been considered. The results show that the hybrid algorithm has better performance compared to the basic algorithms that are employed. In particular, for the rendezvous problem, a 100% efficiency can be obtained both by differential evolution and the genetic algorithm only when particular strategies and parameter settings are adopted. On the other hand, the hybrid algorithm always attains the global optimum, even though nonoptimal strategies and parameter settings are adopted. Also the number of function evaluations, which must be performed to attain the optimum, is reduced when the hybrid algorithm is used. In the case of Earth-Mars missions, the hybrid algorithm is successfully employed to determine mission opportunities in a large search space.
A hybrid evolutionary algorithm is applied to the optimization of space missions with multiple impulses and gravity assists. The optimization procedure runs three different optimizers, based on genetic algorithms, differential evolution and particle swarm optimization, in parallel; the algorithms are used synergistically by letting the best individuals, found by each algorithm, migrate to the others at prescribed intervals. A mass mutation operator is also employed to diversify the population and avoid premature convergence to suboptimal solutions. A module based on an enhanced continuous tabu search algorithm is introduced in the initialization process to produce a good starting population for the optimization algorithm. The results show the good performance obtained with the hybrid algorithm and the improvement in terms of efficiency and computational cost which is provided, in most cases, by the tabu search initialization process.
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