Keywords: multi-objective optimization, composites, memetic algorithm, learning, non-domination, co-evolution..

The optimal design of hybrid composite stiffened structures considering sizing, topology and material selection is addressed in a multi-objective optimization framework is considered in this paper. A structural optimal design approach that simultaneously considers minimum weight or cost and minimum strain energy is presented. The trade-off between the objectives, depending on the stress, displacement and buckling constraints imposed on composite structures, is searched. The design variables are ply angles and ply thicknesses of the shell laminates, the cross section dimensions of stiffeners and the variables related to material distribution. Multi-objective memetic algorithm (MOMA) searching pareto-optimal front based on non-dominance concepts is proposed. The MOMA is an evolutionary algorithm based on Darwinian principles together with learning procedures using Dawkins concepts. MOMA applies multiple learning procedures exploring the synergy of different cultural transmission rules. The approach is based on multiple populations, species conservation, migration, self-adaptive, local search, controlled mutation, age control and features-based alleles statistics. The MOMA is able to indicate alternative optimal designs that might be very important for the designers in multi-objective design optimization of stiffened composite structures.

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