Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Artificial immune system for multi-objective design optimization of composite structures
Engineering Applications of Artificial Intelligence
Engineering optimizations via nature-inspired virtual bee algorithms
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Swarm intelligence approaches to estimate electricity energy demand in Turkey
Knowledge-Based Systems
Identification of structural models using a modified Artificial Bee Colony algorithm
Computers and Structures
International Journal of Applied Metaheuristic Computing
An artificial bee colony algorithm for the maximally diverse grouping problem
Information Sciences: an International Journal
A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing
Applied Soft Computing
A survey of non-gradient optimization methods in structural engineering
Advances in Engineering Software
Advances in Artificial Intelligence
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
A hybrid metaheuristic for the cyclic antibandwidth problem
Knowledge-Based Systems
Engineering Applications of Artificial Intelligence
Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms
Journal of Global Optimization
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC 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. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations.