Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

Authors:
S. N. Omkar;Dheevatsa Mudigere;G. Narayana Naik;S. Gopalakrishnan
Affiliations:
Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India
Venue:
Computers and Structures
Year:
2008

Citing 8
Cited 12

Particle swarm optimization method in multiobjective problems

Proceedings of the 2002 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Fundamentals of Computational Swarm Intelligence

Fundamentals of Computational Swarm Intelligence
Multiobjective optimization using dynamic neighborhood particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Prediction of construction litigation outcome using a split-step PSO algorithm

ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Algal bloom prediction with particle swarm optimization algorithm

CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I

Artificial immune system for multi-objective design optimization of composite structures

Engineering Applications of Artificial Intelligence
Differential evolution strategy for structural system identification

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

Expert Systems with Applications: An International Journal
Activity assigning of fourth party logistics by particle swarm optimization-based preemptive fuzzy integer goal programming

Expert Systems with Applications: An International Journal
Analysis and benchmarking of meta-heuristic techniques for lay-up optimization

Computers and Structures
Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

Applied Soft Computing
Global laminate optimization on geometrically partitioned shell structures

Structural and Multidisciplinary Optimization
Survey: Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: A review and state-of-the-art

Computer Science Review
MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

Engineering Applications of Artificial Intelligence
Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures

International Journal of Applied Metaheuristic Computing
A new multi-swarm multi-objective optimization method for structural design

Advances in Engineering Software
Simultaneous design of structural layout and discrete fiber orientation using bi-value coding parameterization and volume constraint

Structural and Multidisciplinary Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables 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 fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented.