Multiobjective Optimization of a Containership using Deterministic Particle Swarm Optimization

The purpose of this paper is to show how the improvement of the hydrodynamics performance of a ship can be obtained by solving a shape optimization problem using the particle swarm optimization (PSO) technique. PSO has been recently introduced to solve global optimization problems and belongs to the class of evolutionary algorithms. In this paper, the basic stochastic algorithm is modified into a deterministic method, eliminating the randomized heuristic search. This algorithm has been then extended to deal with multiobjective problems by following the concept of subswarms and introducing a new strategy for the selection of the subswarm leaders. Two different versions of this strategy are illustrated and compared. Effectiveness and efficiency of the method proposed here are demonstrated by solving a set of algebraic multiobjective test problems, designed to represent a wide selection of possible shapes of the Pareto front. Comparisons with a well-known multiobjective genetic algorithm are also presented. Finally, the new method is used to reduce the heave and pitch motion peaks of the response amplitude operator of a containership advancing at fixed speed in head seas, subject to some real-life constraints. The results confirm the applicability of the developed approach to real ship design problems.