A Novel Hybrid Genetic Modified Colliding Bodies Optimization for Designing of Composite Laminates

This study presents a robust hybrid meta-heuristic optimization algorithm by merging Modified Colliding Bodies Optimization and Genetic Algorithm that is called GMCBO. One of the inabilities of Colliding Bodies Optimization (CBO) is collapsing into the trap of local minima and not finding global optima. In this paper, to rectify this weak point, at first, some modifications are accomplished on the CBO process and then by using the concept of the genetic algorithm able to enhance the convergence rate, establishing a balance between the feature exploration and exploitation processes, the increasing power of finding global optimal design and escaping of local optimal. For evaluating the performance of the proposed method, the optimal design of laminated composite materials has been considered. Compare the results of structural analysis with GMCBO and other optimization methods shows a high convergence rate and its ability to find the global optimal solution of the proposed algorithm for structural optimization problems.

Keywords:

Composite materials , Hybrid meta-heuristic optimization , Colliding bodies optimization , Discrete variable