Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Metaheuristic Optimization in Structural Engineering
Abstract Metaheuristic search methods have been extensively used for optimization of the structures over the past two decades. Genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), harmony search (HS), big bang-big crunch (BB-BC), artificial bee colony algorithm (ABC) and teaching–learning-based optimization (TLBO) are the most popular metaheuristic optimization methods. The basic principle of these methods is that they make an analogy between the natural phenomena and the optimization problems. In this chapter, recently developed metaheuristic optimization methods such as self-adaptive harmony search and teaching–learning-based optimization are reviewed and the performance of these methods in the field of structural engineering are compared with each other and the other metaheuristic methods.
Metaheuristic Optimization in Structural Engineering
Abstract Metaheuristic search methods have been extensively used for optimization of the structures over the past two decades. Genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), harmony search (HS), big bang-big crunch (BB-BC), artificial bee colony algorithm (ABC) and teaching–learning-based optimization (TLBO) are the most popular metaheuristic optimization methods. The basic principle of these methods is that they make an analogy between the natural phenomena and the optimization problems. In this chapter, recently developed metaheuristic optimization methods such as self-adaptive harmony search and teaching–learning-based optimization are reviewed and the performance of these methods in the field of structural engineering are compared with each other and the other metaheuristic methods.
Metaheuristic Optimization in Structural Engineering
Degertekin, S. O. (Autor:in) / Geem, Zong Woo (Autor:in)
11.12.2015
19 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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