Abstract
The paper presents an alternative constraint-handling technique that converts a nonlinear constrained programming problem into an unconstrained multi-objective optimisation problem. The technique is derived from the behavioural memory constraint-handling method, which was originally implemented for single-objective optimisation with genetic algorithms. We compare our presented technique with two other popular constraint-handling concepts and demonstrate its superiority over them when applied to a propeller optimisation problem. We conclude that the multi-objective behavioural memory constraint-handling technique conjugated with the non-dominated sorting genetic algorithm (NSGA-II) is a prudent method to apply to problems with an infeasible initial design and where constraints have a natural order of satisfaction, which, if not conformed to, would lead to unrealistic designs that impair the search by GA.
Similar content being viewed by others
References
Abbott IH, Von Doenhoff AE (1959) Theory of wing sections. Dover, New York
Benini E (2003) Multiobjective design optimization of b-screw series propellers using evolutionary algorithms. Mar Technol 40(4):229–238
Cahon S, Melab N, Talbi EG (2004) Paradiseo: a framework for the reusable design of parallel and distributed metaheuristics. J Heuristics 10(3):357–380
Chen JH, Shih YS (2007) Basic design of a series propeller with vibration consideration by genetic algorithm. J Mar Sci Technol 12:119–129
Coello CAC (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inf Syst 1(3):269–308
Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287
Coello Coello CA (2009) Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Front. Comput. Sci. China 3(1):18–30
Das I, Dennis JE (1998) Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J Control Optim 8:631–657
Davis L (1987) Genetic algorithms and simulated annealing. Pitman, London
de Garis H (1990) Genetic programming: building artificial nervous systems using genetically programmed neural networks modules. In: Porter R, Mooney B (eds) Proceedings of the 7th international conference on machine learning. Morgan Kaufmann, San Mateo, pp 132–139
Deb K (2000) An efficient constraint-handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York
Deb K (2008) A robust evolutionary framework for multi-objective optimization. In: Proceedings of the 10th annual conference on genetic and evolutionary computation. ACM, New York, pp 633–640
Deb K, Pratap A, Agarwal S, Maeyarivan T (2000) A fast and elitist multi-objective genetic algorithm: NSGA-II. In: Parallel problem solving from nature—PPSN VI. Springer, Berlin, pp 849–858
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):181
Eckhardt M, Morgan W (1955) A propeller design method. Trans SNAME 63
Han KJ (2008) Numerical optimization of hull/propeller/rudder configurations. PhD thesis, Chalmers University of Technology
Han KJ, Bark G, Larsson L, Regnstrom B (2006) A procedure for optimizing cavitating propeller blades in a given wake. Ship Technol Res 53(1):39–52
Keller J (1966) Enige aspecten bij het ontwerpen van scheepshchroeven. Schip Werf 24(33):658
Le Riche R, Knopf-Lenoir C, Haftka R (1995) A segregated genetic algorithm for constrainted structural optimization. In: Eshelman L (ed) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, San Mateo, pp 558–565
Lee JT (1987) Potential based panel method for the analysis of marine propellers in steady flow. PhD thesis, Department of Ocean Engineering, M.I.T
Marzi J (2008) Virtue—European approach to developing the numerical towing tank. In: RINA CFD
Mezura-Montes E, Coello Coello CA (2008) Constrained optimization via multiobjective evolutionary algorithms. In: Natural computing series. Springer, Berlin, pp 53–75
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, Berlin
Mishima S (1996) Design of cavitating propeller blades in non-uniform flaw by numerical optimization. PhD thesis, Massachusetts Institute of Technology
Mishima S, Kinnas S (1997) Application of a numerical optimization technique to the design of cavitating propellers in nonuniform flow. J Ship Res 41(2):93–107
Oosterveld M, van Oossanen P (1975) Further computer-analyzed data of the Wageningen b-screw series. Int Shipbuild Prog 22(251):251–262
Oyama A (2009) Constraint-handling in evolutionary aerodynamic design. In: Studies in computational intelligence. Springer, Berlin, pp 219–236
Schoenauer M, Xanthakis S (1993) Constrained ga optimization. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms, pp 573–580. Morgan Kaufmann, San Mateo
Streckwall H (1998) Hydrodynamic analysis of three propellers using a surface panel method for steady and unsteady inflow conditions. In: 22nd ITTC propulsion committee propeller RANS/panel method workshop. Grenoble, France
Streckwall H (2005) Numerical models for cavitation and propeller pressure fluctuations. In: Jahrbuch der Schiffbautechnischen Gesellschaft. Springer, Berlin, pp 44–49
Takekoshi Y, Kawamura T, Yamaguchi H, Maeda M, Ishii N, Kimura K, Taketani T, Fujii A (2005) Study on the design of propeller blade sections using the optimization algorithm. J Mar Sci Technol 10(2):70–81
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Puisa, R., Streckwall, H. Prudent constraint-handling technique for multiobjective propeller optimisation. Optim Eng 12, 657–680 (2011). https://doi.org/10.1007/s11081-010-9133-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11081-010-9133-z