A diversification phase based on tabu search is introduced to improve the performance of an evolutionary algorithm, which runs a genetic algorithm, differential evolution, and particle swarm optimization in parallel, according to the island model. Two problems concerning space trajectory optimization (Cassini mission and a round trip mission to near-Earth asteroids) are considered. These problems present different peculiarities which make the search of the global optimum difficult. It is shown that the probability of success may be, in some cases, quite low, due to the presence of many local optima and/or the existence of a bias towards suboptimal solution. The addition of an initial diversification phase in most cases improves the algorithm capability of finding the global optimum, without significantly increasing (and sometimes even decreasing) the number of function evaluations required to attain the optimum and thus reducing the required computational effort.
D'AngeloS.MinisciE.Multi-objective evolutionary optimization of subsonic airfoils by meta-modelling and evolution control. Proc. IMechE, Part G: J. Aerospace Engineering,, 2007, 221, 805–814.
2.
LeeJ.JangM.Genetic algorithm-based design of transonic aerofoils using Euler equations. Proc. IMechE, Part G: J. Aerospace Engineering,, 2008, 222, 1037–1045.
3.
WatanabeT.MatsushimaK.NakahashiK.Aerodynamic shape optimization of a near-sonic passenger plane using computational fluid dynamics. Proceedings of the IMechE, Part G: J. Aerospace Engineering,, 2008, 222, 1025–1035.
4.
Taghavi-ZenouzR.AfzaliS. M.Preliminary design optimization of profile losses in multi-stage axial compressors based on complex method. Proc. IMechE, Part G: J. Aerospace Engineering,, 2008, 222, 951–961.
5.
BoulkeraaT.GhenaieA.Optimizations of turboprop engines the using nondominated sorting genetic algorithm. Proc. IMechE, Part G: J. Aerospace Engineering,, 2010, 224, 1271–1283.
6.
NooriF.GorjiM.KazemiA.NematiH.Thermodynamic optimization of ideal turbojet with afterburner engines using non-dominated sorting genetic algorithm ii. Proc. IMechE, Part G: J. Aerospace Engineering,, 2010, 224, 1285–1296.
7.
TanW.JenT.-C.GaoM.Medium earth orbit and inclined geosynchronous orbit satellite control strategies optimization based on the function approximation method. Proc. IMechE, Part G: J. Aerospace Engineering,, 2009, 223, 475–484.
8.
LinC.-L.LiY.-H.AoufN.Potential-field-based evolutionary route planner for the control of multiple unmanned aerial vehicles. Proc. IMechE, Part G: J. Aerospace Engineering,, 2010, 224, 1229–1242.
9.
StewartP.GladwinD.ParrM.StewartJ.Multi-objective evolutionary-fuzzy augmented flight control for an f16 aircraft. Proc. IMechE, Part G: J. Aerospace Engineering,, 2010, 224, 293–309.
10.
SaggianiG. M.CaligianaG.PersianiF.Multiobjective wing design using genetic algorithms and fuzzy logic. Proc. IMechE, Part G: J. Aerospace Engineering,, 2004, 218, 133–145.
11.
XuX.LuoY.-Z.Multi-objective shape control of prestressed structures with genetic algorithms. Proc. IMechE, Part G: J. Aerospace Engineering,, 2008, 222, 1139–1147.
12.
BiesbroekR.Study of genetic algorithm settings for trajectory optimisation, 2003 (IAF) Paper IAF-03-A.P.30.
13.
BiesbroekR.A comparison of differential evolution method with genetic algorithms for orbit optimisation., 2006 (IAF) Paper IAF-06-C1.4.02.
14.
Di LiziaP.RadiceG.Advanced global optimization tools for mission analysis and design. Final Report of ESA Ariadna, ESA, 2004, 2004 (Final Report of ESA Ariadna, ESA.
15.
VasileM.De PascaleP.Preliminary design of multiple gravity-assist trajectories. J. Spacecraft Rockets,, 2006, 43, 794–805.
16.
BessetteC.SpencerD.Optimal space trajectory design: A heuristic-based approach. Adv. Astronautical Sci.,, 2006, 124, 1611–1628.
17.
IzzoD.BecerraV.MyattD.NasutoS.BishopJ.Search space pruning and global optimization of multiple gravity assist spacecraft trajectories. J. Global Optimization,, 2007, 38, 283–296.
18.
VinkoT.IzzoD.BombardelliC.Benchmarking different global optimisation techniques for preliminary space trajectory design., 2007 (IAF) Paper IAC-07-A1.3.01.
19.
OldsA.KlueverC.CupplesM.Interplanetary mission design using differential evolution. J. Spacecraft and Rockets,, 2007, 44, 1060–1070.
20.
VasileM.MinisciE.LocatelliM.Analysis of some global optimization algorithms for space trajectory design. J. Spacecraft and Rockets,, 2010, 47, 334–344.
21.
Rosa SentinellaM.Comparison and integrated use of differential evolution and genetic algorithms for space trajectory optimisation. In Proceedings of the 2007 IEEE Congress on Evolutionary computation, Singapore, 25–28 September, 2007973–978 (IEEE, Piscataway, New Jersey).
22.
Rosa SentinellaM.CasalinoL.Hybrid evolutionary algorithm for the optimization of interplanetary trajectories. J. Spacecraft and Rockets,, 2009, 46, 365–372.
23.
Rosa SentinellaM.CasalinoL.Cooperative evolutionary algorithm for space trajectory optimization. Celestial Mech. Dyn. Astron., 2009, 105, 211–227.
24.
CasalinoL.ColasurdoG.PastroneD.Simple strategy for powered swingby. J. Guidance, Control, Dyn., 1999, 22, 156–159.
25.
HollandJ. H.Adaptation in natural and artificial systems, 1975 (University of Michigan Press, Ann Arbor, Michigan).
26.
DebK.An introduction to genetic algorithms. Sādhanā J.,, 2009, 24, 293–315.
27.
StornR.PriceK.Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces., 1995 (ICSI) TR–95–012.
28.
StornR.On the usage of differential evolution for function optimization. 19961996 Biennial Conference of the North American Fuzzy Information Processing Society, Berkeley, California, 19–22 June (IEEE, Piscataway, New Jersey) 519–523.
29.
KennedyJ.EberhartR.Particle swarm optimisation. 1995Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 27 November–1 December (IEEE, Piscataway, New Jersey) 1942–1948.
