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RISS 인기검색어
- 검색키워드
- 저자 : Erkan Doğan (검색결과 3 건)
- 무료
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Solving design optimization problems via hunting search algorithm with Levy flights
Erkan Doğan 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.52 No.2
This study presents a hunting search based optimum design algorithm for engineering optimization problems. Hunting search algorithm is an optimum design method inspired by group hunting of animals such as wolves, lions, and dolphins. Each of these hunters employs hunting in a different way. However, they are common in that all of them search for a prey in a group. Hunters encircle the prey and the ring of siege is tightened gradually until it is caught. Hunting search algorithm is employed for the automation of optimum design process, during which the design variables are selected for the minimum objective function value controlled by the design restrictions. Three different examples, namely welded beam, cellular beam and moment resisting steel frame are selected as numerical design problems and solved for the optimum solution. Each example differs in the following ways: Unlike welded beam design problem having continuous design variables, steel frame and cellular beam design problems include discrete design variables. Moreover, while the cellular beam is designed under the provisions of BS 5960, LRFD-AISC (Load and Resistant Factor Design-American Institute of Steel Construction) is considered for the formulation of moment resisting steel frame. Levy Flights is adapted to the simple hunting search algorithm for better search. For comparison, same design examples are also solved by using some other well-known search methods in the literature. Results reveal that hunting search shows good performance in finding optimum solutions for each design problem.
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An improved particle swarm optimizer for steel grillage systems
Ferhat Erdal,Erkan Doğan,Mehmet Polat Saka 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.4
In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.
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Structure Optimization with Metaheuristic Algorithms and Analysis by Finite Element Method
Betül Üstüner,Erkan Doğan 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.1
In engineering, design is made by considering functionality, reliability, manufacturability, usability, and total cost. There are a wide variety of methods for design optimization. Metaheuristic methods inspired by nature are one of them. In this study, the Refinement firefly algorithm is proposed as a new method. Grey Wolf, Particle Swarm, and Firefly algorithms are compared with the proposed Refinement Firefly Algorithm. Mathematical benchmark problems are used to examine the performance of algorithms. Also, welded beam, cellular beam, and frame system designs are considered sample problems. These design examples are solved by algorithms and the sections are determined. The sections determined by optimization were analyzed using the ABAQUS CAE program and its reliability was examined. Numerical analysis with the finite element method is very useful as it provides realistic solutions. ABAQUS CAE is used to detect and show deformations in the structure. Finite element solution with ABAQUS solves the problems analytically and it is seen that the sections determined by the optimum design algorithm remain within the limits. The proposed Refinement Firefly algorithm demonstrates superior performance compared to the Firefly algorithm. However, it yields inferior results when compared to the Grey Wolf and Particle Swarm algorithms.
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