Journal ArticleDOI
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Ali Kaveh,Siamak Talatahari +1 more
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TLDR
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.About:
This article is published in Computers & Structures.The article was published on 2009-09-01. It has received 387 citations till now. The article focuses on the topics: Metaheuristic & Meta-optimization.read more
Citations
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Journal ArticleDOI
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
Journal ArticleDOI
A novel heuristic optimization method: charged system search
Ali Kaveh,Siamak Talatahari +1 more
TL;DR: A comparison of the results with those of other evolutionary algorithms shows that the proposed algorithm outperforms its rivals.
Journal ArticleDOI
Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization
TL;DR: Simulation results demonstrate that TSA generates better optimal solutions in comparison to other competitive algorithms and is capable of solving real case studies having unknown search spaces.
Journal ArticleDOI
Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems
Gaurav Dhiman,Vijay Kumar +1 more
TL;DR: Experimental results reveal that the proposed SOA algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.
Proceedings ArticleDOI
Elephant Herding Optimization
TL;DR: A new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks, inspired by the herding behavior of elephant group.
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.
Book
An Introduction to Genetic Algorithms
TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Book
Simulated Annealing: Theory and Applications
TL;DR: Performance of the simulated annealing algorithm and the relation with statistical physics and asymptotic convergence results are presented.
Related Papers (5)
Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures
Ali Kaveh,Siamak Talatahari +1 more
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