Multi-objective Memetic Algorithm Based on Learning for Sustainable Design of FRP Composite Structure

This approach aims for decreasing costs in lightweight structures using FRP composite materials based on a hybrid construction where expensive and high-stiffness materials performs together with inexpensive and low-stiffness material. The proposed optimal design of hybrid composite stiffened structures addresses sizing, topology and sustainable material selection in a multi-objective optimization framework. Minimum weight (cost) and minimum strain energy (stiffness) associated with sustainable factors are the objectives of the proposed structural robust design approach. The model performs the trade-off between the performance targets against sustainability, depending on given stress, displacement and buckling constraints imposed on composite structures. The design variables are ply angles and ply thicknesses of shell laminates, the cross section dimensions of stiffeners and the variables related to material selections’ and structural distribution. A Multi-objective Memetic Algorithm (MOMA) searching Pareto-optimal front is proposed. MOMA applies multiple learning procedures exploring the synergy of different cultural transmission rules. These rules are associated with some kind of problem knowledge and learning classified as Lamarckian or Baldwinian. The memetic learning procedures aim to improve the exploitation and exploration capacities of MOMA as shown by the numerical simulations.