Niching particle swarm optimization techniques for multimodal buckling maximization of composite laminates

Abstract

It is natural to extend conventional unimodal optimization to challenging multimodal optimization design of composite structures by means of emergent niching particle swarm optimization (PSO), due to multimodal characteristics of composite structures by nature. The advanced multimodal PSO algorithms adopted in the present study include the species-based PSO (SPSO), the fitness Euclidean-distance ratio based PSO (FER-PSO), the ring topology based PSO and the Euclidean distance-based locally informed particle swarm (LIPS) optimizer, which are applied to a multimodal buckling maximization problem of composite panels. SPSO, FER-PSO, the ring topology based PSO and the variant of LIPS succeed to simultaneously indentify not only the first-best-fitness solutions (the global optima) but also the second-best-fitness solutions (the global sub-optima) to this buckling optimization design in a single optimization process. The fifteen second-best-fitness solutions are discovered for the first time and the buckling-resistance difference between the seven first-best-fitness solutions and the fifteen second-best-fitness solutions is definitely negligible from the viewpoint of practical applications. Based on the new findings of twenty-two acceptable solutions, this specific buckling maximization design of composite panels can be served as a benchmark problem of real-world multimodal optimization. In terms of the statistical outcomes on algorithmic performance assessments, the ring topology based PSO without any niching parameter is suitable and reliable for multimodal optimization of composite structures in comparison with SPSO, FER-PSO and the variant of LIPS.