Application of derivative-free multi-objective algorithms to reliability-based robust design optimization of a high-speed catamaran in real ocean environment

A reliability-based robust design optimization (RBRDO) for ship hulls is presented. A real ocean environment is considered, including stochastic sea state and speed. The optimization problem has two objectives: (a) the reduction of the expected value of the total resistance in waves and (b) the increase of the ship operability (reliability). Analysis tools include a URANS solver, uncertainty quantification methods and metamodels, developed and validated in earlier research. The design space is defined by an orthogonal four- dimensional representation of shape modifications, based on the Karhunen-Lo` eve expansion of free-form defor- mations of the original hull. The objective of the present paper is the assessment of deterministic derivative-free multi-objective optimization algorithms for the solution of the RBRDO problem, with focus on multi-objective extensions of the deterministic particle swarm optimization (DPSO) algorithm. Three evaluation metrics provide the assessment of the proximity of the solutions to a reference Pareto front and their wideness.

[1]  Ching-Lai Hwang,et al.  Methods for Multiple Objective Decision Making , 1979 .

[2]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[3]  Tien-Tsin Wong,et al.  Sampling with Hammersley and Halton Points , 1997, J. Graphics, GPU, & Game Tools.

[4]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[5]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[6]  F. Cheng,et al.  GENERALIZED CENTER METHOD FOR MULTIOBJECTIVE ENGINEERING OPTIMIZATION , 1999 .

[7]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[8]  Yaochu Jin,et al.  Dynamic Weighted Aggregation for evolutionary multi-objective optimization: why does it work and how? , 2001 .

[9]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[10]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[11]  Bernhard Sendhoff,et al.  On Test Functions for Evolutionary Multi-objective Optimization , 2004, PPSN.

[12]  R. Lyndon While,et al.  A Scalable Multi-objective Test Problem Toolkit , 2005, EMO.

[13]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[14]  M. Clerc Stagnation Analysis in Particle Swarm Optimisation or What Happens When Nothing Happens , 2006 .

[15]  A. Lovison A SYNTHETIC APPROACH TO MULTIOBJECTIVE OPTIMIZATION , 2010 .

[16]  Carlos A. Coello Coello,et al.  Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer That Uses a Very Small Population Size , 2010, Multi-Objective Swarm Intelligent System.

[17]  Daniele Peri,et al.  ON THE USE OF SYNCHRONOUS AND ASYNCHRONOUS SINGLE-OBJECTIVE DETERMINISTIC PARTICLE SWARM OPTIMIZATION IN SHIP DESIGN PROBLEMS , 2014 .

[18]  K. K. Choi,et al.  Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification , 2014, Structural and Multidisciplinary Optimization.

[19]  X. Chen,et al.  High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm , 2015 .