Journal ArticleDOI
Particle swarm approach for structural design optimization
Ruben E. Perez,Kamran Behdinan +1 more
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TLDR
Improvements, effect of the different setting parameters, and functionality of the algorithm are shown in the scope of classical structural optimization problems, and results show the ability of the proposed methodology to find better optimal solutions for structural optimization tasks than other optimization algorithms.About:
This article is published in Computers & Structures.The article was published on 2007-10-01. It has received 646 citations till now. The article focuses on the topics: Multi-swarm optimization & Metaheuristic.read more
Citations
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The best-so-far selection in Artificial Bee Colony algorithm
TL;DR: The proposed modified method for solution update of the onlooker bees is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm.
Journal ArticleDOI
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Ali Kaveh,Siamak Talatahari +1 more
TL;DR: A Hybrid Big Bang-Big Crunch (HBB-BC) optimization algorithm is employed for optimal design of truss structures and numerical results demonstrate the efficiency and robustness of the H BB-BC method compared to other heuristic algorithms.
Journal ArticleDOI
Artificial Bee Colony algorithm for optimization of truss structures
TL;DR: The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design.
Journal ArticleDOI
Review of performance optimization techniques applied to wind turbines
TL;DR: A review of the optimization techniques and strategies applied to wind turbine performance optimization is presented in this paper by identifying the most significant objectives, targets and issues, as well as the optimization formulations, schemes and models available in the published literature.
Journal ArticleDOI
Comparison of evolutionary-based optimization algorithms for structural design optimization
TL;DR: The results show that the proposed approach gives better solutions compared to genetic algorithm, particle swarm, immune algorithm, artificial bee colony algorithm and differential evolution algorithm that are representative of the state-of-the-art in the evolutionary optimization literature.
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.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.