A new multi-objective optimizer based on swarm intelligence is presented in this article. A distinctive feature of the proposed particle swarm optimizer (PSO) is the utilization of only social components, which are based on global guides, for the exploration and exploitation of the search space. Mutation and elitism are also employed in order to improve the effectiveness of the PSO. The algorithmic parameters are controlled via an on-line adaptive scheme. The algorithm is further developed to co-evolve multiple swarms. The investigation of various multi-objective optimization problems reveals that the proposed PSO is able to converge fast and in a robust manner towards the true Pareto-optimal front. Comparisons with results obtained from other multi-objective optimizers are presented. A parametric investigation is performed in order to exploit the potential of the proposed co-evolutionary algorithm for parallelization. The results obtained from a hydrofoil design optimization problem demonstrate near-linear speedup and high parallel efficiency.

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In this article, the optimization problem of designing transonic airfoil sections is solved using a framework based on a multi-objective optimizer and surrogate models for the objective functions and constraints. The computed Pareto-optimal set includes solutions that provide a trade-off between maximizing the lift-to-drag ratio during cruise and minimizing the trailing edge noise during the aircraft's approach to landing. The optimization problem was solved using a recently developed multi-objective optimizer, which is based on swarm intelligence. Additional computational intelligence tools, e.g., artificial neural networks, were utilized to create surrogate models of the objective functions and constraints. The results demonstrate the effectiveness and efficiency of the proposed optimization framework when applied to simulation-based engineering design optimization problems.

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In order to clarify the existence and amount of a required propulsion power reduction possible with the efficient use of the Ballast-Free Ship concept on a Seaway-size bulk carrier, additional experimental and computational hydrodynamics studies were undertaken during the past year. Experimental studies performed in the University of Michigan Marine Hydrodynamics Laboratory (MHL) are described. Computational fluid dynamics (CFD) investigations performed using Star-CCMþ at both model and full scale are also presented.