Journal ArticleDOI
Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures
S. N. Omkar,J. Senthilnath,Rahul Khandelwal,G. Narayana Naik,Srinivasan Gopalakrishnan +4 more
- Vol. 11, Iss: 1, pp 489-499
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TLDR
The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations and is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA).Abstract:
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 configurationsread more
Citations
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Journal ArticleDOI
Multiobjective evolutionary algorithms: A survey of the state of the art
TL;DR: This paper surveys the development ofMOEAs primarily during the last eight years and covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEas, coevolutionary MOE As, selection and offspring reproduction operators, MOE as with specific search methods, MOeAs for multimodal problems, constraint handling and MOE
Journal ArticleDOI
A comprehensive survey: artificial bee colony (ABC) algorithm and applications
TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Journal ArticleDOI
A survey on new generation metaheuristic algorithms
TL;DR: In this survey, fourteen new and outstanding metaheuristics that have been introduced for the last twenty years other than the classical ones such as genetic, particle swarm, and tabu search are distinguished.
Journal ArticleDOI
A social spider algorithm for global optimization
James J.Q. Yu,Victor O. K. Li +1 more
TL;DR: Zhang et al. as discussed by the authors proposed a new nature-inspired social-spider-based swarm intelligence algorithm, which is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys.
Journal ArticleDOI
A multi-objective artificial bee colony algorithm
TL;DR: The proposed algorithm was evaluated on a set of standard test problems in comparison with other state-of-the-art algorithms and results indicate that the proposed approach is competitive compared to other algorithms considered in this work.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Lecture Notes in Artificial Intelligence
P. Brezillon,P. Bouquet +1 more
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
Journal ArticleDOI
On the performance of artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.