In this study, the multi-objective optimization using genetic algorithm is carried out for optimization of the thermodynamic cycle of ideal turbojet with afterburner engines. The fast non-dominated sorting genetic algorithm II is used to obtain the solutions. The possible important conflicting thermodynamic objective functions that have been considered in this work are specific thrust, thrust-specific fuel consumption, propulsive efficiency (ηp), and thermal efficiency (ηt). Different pairs of these objective functions have been selected for two-objective optimization processes. These objectives have also been considered for a four-objective optimization problem using the multi-objective genetic algorithm of this work. The research demonstrates that the results of four-objective optimization can include those of two-objective optimization and, therefore, provide more choices for the optimal design of the thermodynamic cycle of ideal turbojet with afterburner engines.
RennerG.EkartA.Genetic algorithms in computer aided designComput. Aided Des.200335, 8, 709–726
7.
SrinivasN.DebK.Multiobjective optimization using non-dominated sorting in genetic algorithmsEvol. Comput.19942, 3, 221–248
8.
FonsecaC. M.FlemingP. J.ForrestS.Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization 1993 In Proceedings of the Fifth International Conference on Genetic algorithms San Mateo, California Morgan Kaufmann, 416–423
9.
Coello CoelloC. A.ChristiansenA. D.Multiobjective optimization of trusses using genetic algorithmsComput. Struct.200075, 6, 647–660
SchafferJ. D.Multiple objective optimization with vector evaluated genetic algorithms1985In Proceedings of the First International Conference on Genetic algorithms and their applicationsPittsburgh, USACarnegie-Mellon University, 93–100
12.
13.
HornJ.NafpliotisN.GoldbergD. E.A niched Pareto genetic algorithm for multiobjective optimization 1994 1, In Proceedings of the First IEEE Conference on Evolutionary computation, IEEE World Congress on Computational intelligence Orlando, Florida London IEEE, 82–87
MurataT.IshibuchiH.MOGA: Multi-objective genetic algorithms19951, In Proceedings of the 1995 IEEE International Conference on Evolutionary computationWestern Australia, AustraliaIEEE, 289–294, London
16.
ZitzlerE.ThieleL.Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approachIEEE Trans. Evol. Comput.19993, 4, 257–271
17.
18.
KnowlesJ. D.CorneD. W.Approximating the non-dominated front using the Pareto archived evolution strategyEvol. Comput.20008, 2, 149–172
19.
CorneD. W.KnowlesJ. D.OatesM. J.The Pareto envelope-based selection algorithm for multiobjective optimization 2000In Proceedings of the Sixth International Conference on Parallel problem solving from nature Paris, France Springer, 839–848
20.
CorneD. W.JerramN. R.KnowlesJ. D.OatesM. J.PESA-II: Region-based selection in evolutionary multiobjective optimization 2001 In Proceedings of the Genetic and Evolutionary Computation Conference San Francisco, California, 283–290
21.
DebK.PratapA.AgarwalS.MeyarivaT.A fast and elitist multi-objective genetic algorithm: NSGA-IIIEEE Trans. Evol. Comput.20026, 2, 182–197
22.
SarkerR.LiangK. H.NewtonC.A new multi-objective evolutionary algorithmEur. J. Oper. Res.2002140, 1, 12–23
23.
CoelloC. A. C.PulidoG. T.A micro-genetic algorithm for multiobjective optimization2001In Proceedings of the First International Conference on, Evolutionary multi-criterion optimization, Zurich, Switzerland, EMO, 681–695, (Springer)
24.
LuH.YenG. G.Rank-density-based multi-objective genetic algorithm and benchmark test function studyIEEE Trans. Evol. Comput.20037, 4, 325–343
25.
YenG. G.LuH.Dynamic multiobjective evolutionary algorithm: Adaptive cell-based rank and density estimationIEEE Trans. Evol. Comput.20037, 3, 253–274
26.
27.
ZitzlerE.DebK.ThieleL.Comparison of multi objective evolutionary algorithms: Empirical resultsEvol. Comput.20008, 2, 173–175
28.
29.
ToffoloA.LazzarettoA.Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system designEnergy200227, 6, 549–567
30.
WrightJ. A.LoosemoreH. A.FarmaniR.Optimization of building thermal design and control by multi-criterion genetic algorithmEnergy Build.200234, 959–972
31.
RoosenP.UhlenbruckS.LucasK.Pareto optimization of a combined cycle power system as a decision support tool for trading off investment vs. operating costsInt. J. Therm. Sci.200342, 6, 553–560
32.
HomaifarA.LaiH. Y.McCormickE.System optimization of turbofan engines using genetic algorithmsAppl. Math. Model.199418, 2, 72–83
33.
AtashkariK.Nariman-ZadehN.PilechiA.JamaliA.YaoX.Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithmsInt. J. Therm. Sci.200544, 11, 1061–1071
34.
FarahatS.Khorasani NejadE.Hoseini SarvariS. M.Thermodynamic optimization of turboshaft engine using multi-objective genetic algorithmWorld Acad. Sci. Eng. Technol.200956, 782–788
35.
CoelloC. A.BecerraR. L.Evolutionary multi-objective optimization using a cultural algorithm20031, In Proceedings of the IEEE Swarm Intelligence Symposium, IEEE Service CenterPiscataway, New Jersey, 6–13
36.
Van VeldhuizenD.Multi-objective evolutionary algorithms: Classifications, analyses, and new innovations1999Dayton, OhioAir force Institute of Technology, PhD Dissertation and Technical report no. AFIT/DS/99-01
37.
DebK.Multi-objective genetic algorithms: Problem difficulties and construction of test FunctionEvol. Comput.19997, 205–230
38.
39.
DebK.Unveiling innovative design principles by means of multiple conflicting objectivesEng. Optim.200335, 5, 445–470
