Enhanced Continuous Tabu Search in a Hybrid Evolutionary Algorithm for the Optimization of Interplanetary Trajectories

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.