ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

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

Omkar, SN and Senthilnath, J and Khandelwal, Rahul and Naik, Narayana G and Gopalakrishnan, S (2011) Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures. In: Applied Soft Computing, 11 (1). pp. 489-499.

[img] PDF
abc.pdf - Published Version
Restricted to Registered users only

Download (486kB) | Request a copy
Official URL: http://dx.doi.org/10.1016/j.asoc.2009.12.008


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. (C) 2009 Elsevier B.V. All rights reserved.

Item Type: Journal Article
Publication: Applied Soft Computing
Publisher: Elsevier Science
Additional Information: Copyright of this article belongs to Elsevier Science.
Keywords: Swarm intelligence; Artificial Bee Colony (ABC); Vector Evaluated Artificial Bee Colony (VEABC); Composite; Structural optimization; Multi-objective optimization.
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 27 Sep 2010 11:30
Last Modified: 07 Jul 2011 06:58
URI: http://eprints.iisc.ac.in/id/eprint/32487

Actions (login required)

View Item View Item