Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures

• Published in: Expert systems with applications Vol. 36; no. 8; pp. 11312 - 11322
• Main Authors: Omkar, S.N., Khandelwal, Rahul, Ananth, T.V.S., Narayana Naik, G., Gopalakrishnan, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2009
• Subjects: Composite; Multi-objective optimization; Particle Swarm Optimization (PSO); Quantum behaved Particle Swarm Optimization (QPSO); Structural optimization; Quantum behaved Particle Swarm Optimization (QPSO); Multi-objective optimization; Composite; Structural optimization; Particle Swarm Optimization (PSO)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2009.03.006

We present a new, generic method/model for multi-objective design optimization of laminated composite components using a novel multi-objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm. QPSO is a co-variant of the popular Particle Swarm Optimization (PSO) and has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are – the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; Failure Mechanism based Failure criteria, Maximum stress failure criteria and the Tsai–Wu Failure criteria. The optimization method is validated for a number of different loading configurations – uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences as well as fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Also, the performance of QPSO is compared with the conventional PSO.