Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic algorithms for modelling and optimisation
Journal of Computational and Applied Mathematics - Special issue: Mathematics applied to immunology
An immunity approach to strategic behavioral control
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Multiobjective optimization using ideas from the clonal selection principle
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Constrained multi-objective optimization using steady state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An English Letter Recognition Algorithm Based Artificial Immune
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Study of feature selection for the stored-grain insects based on artificial immune algorithm
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures
Applied Soft Computing
Design optimization of laminated composites using a new variant of simulated annealing
Computers and Structures
A hybrid approach based on MEP and CSP for contour registration
Applied Soft Computing
Comparison of evolutionary-based optimization algorithms for structural design optimization
Engineering Applications of Artificial Intelligence
International Journal of Applied Metaheuristic Computing
Relationships of swarm intelligence and artificial immune system
International Journal of Bio-Inspired Computation
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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 artificial immune system (AIS) paradigm. A co-variant of the popular clonal selection principle called as the Objective Switching Clonal Selection Algorithm (OSCSA) 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.