Applying particle swarm optimization algorithm to roundness measurement
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
QoS multicast routing using a quantum-behaved particle swarm optimization algorithm
Engineering Applications of Artificial Intelligence
Parameters identification of nonlinear state space model of synchronous generator
Engineering Applications of Artificial Intelligence
Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Particle swarm optimization with query-based learning for multi-objective power contract problem
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Hi-index | 12.05 |
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.