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Design Optimization of Multi-objective Structural Engineering Problems Via Artificial Bee Colony Algorithm
The construction sector constitutes a significant portion of global gross national expenditures with huge financial budget requirements and provides employment for more than one hundred million people. Besides, considering that people spend more than 80% of their time indoors today, it is necessary to make optimal structure designs. This requirement stems from the inadequacy of existing structures in the face of today's changing conditions. Indeed, realistic design optimization of the structures can be done not only by taking into account a single objective but also considering a number of structural criteria. It means that there is inherent multi-purpose in most structural design optimization problems. Thus, it is very difficult engineering task to solve these kinds of problems, as it is necessary to optimize multiple purposes simultaneously to obtain optimal designs. With the help of the improvisation in optimization techniques used for multi-objective structural engineering design, algorithms are provided to achieve the optimal designs by creating a strong synergy between the structural requirements and constraints mentioned in the design specifications. The recent addition to this trend is so-called Artificial Bee Colony (ABC) algorithm which simulates the nectar searching ability of the bees in nature for nutrition. In this chapter, an optimal design algorithm via ABC is proposed in order to obtain the optimum design of multi-objective structural engineering design problems. The applications in design examples have shown the robustness, effectiveness, and reliability of ABC in attaining the design optimization of multi-objective constrained structural engineering design problems.
Design Optimization of Multi-objective Structural Engineering Problems Via Artificial Bee Colony Algorithm
The construction sector constitutes a significant portion of global gross national expenditures with huge financial budget requirements and provides employment for more than one hundred million people. Besides, considering that people spend more than 80% of their time indoors today, it is necessary to make optimal structure designs. This requirement stems from the inadequacy of existing structures in the face of today's changing conditions. Indeed, realistic design optimization of the structures can be done not only by taking into account a single objective but also considering a number of structural criteria. It means that there is inherent multi-purpose in most structural design optimization problems. Thus, it is very difficult engineering task to solve these kinds of problems, as it is necessary to optimize multiple purposes simultaneously to obtain optimal designs. With the help of the improvisation in optimization techniques used for multi-objective structural engineering design, algorithms are provided to achieve the optimal designs by creating a strong synergy between the structural requirements and constraints mentioned in the design specifications. The recent addition to this trend is so-called Artificial Bee Colony (ABC) algorithm which simulates the nectar searching ability of the bees in nature for nutrition. In this chapter, an optimal design algorithm via ABC is proposed in order to obtain the optimum design of multi-objective structural engineering design problems. The applications in design examples have shown the robustness, effectiveness, and reliability of ABC in attaining the design optimization of multi-objective constrained structural engineering design problems.
Design Optimization of Multi-objective Structural Engineering Problems Via Artificial Bee Colony Algorithm
Studies in Systems, Decision and Control
Nigdeli, Sinan Melih (Herausgeber:in) / Bekdaş, Gebrail (Herausgeber:in) / Kayabekir, Aylin Ece (Herausgeber:in) / Yucel, Melda (Herausgeber:in) / Carbas, Serdar (Autor:in) / Ustun, Deniz (Autor:in) / Toktas, Abdurrahim (Autor:in)
05.12.2020
26 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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