• Title/Summary/Keyword: modified hyper-sphere algorithm

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An improved algorithm in railway truss bridge optimization under stress, displacement and buckling constraints imposed on moving load

  • Mohammadzadeh, Saeed;Nouri, Mehrdad
    • Structural Engineering and Mechanics
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    • v.46 no.4
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    • pp.571-594
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    • 2013
  • Railway truss bridges are amongst the essential structures in railway transportation. Minimization of the construction and maintenance costs of these trusses can effectively reduce investments in railway industries. In case of railway bridges, due to high ratio of the live load to the dead load, the moving load has considerable influence on the bridge dynamics. In this paper, optimization of the railway truss bridges under moving load is taken into consideration. The appropriate algorithm namely Hyper-sphere algorithm is used for this multifaceted problem. Through optimization the efficiency of the method successfully raised about 5 percent, compared with similar algorithms. The proposed optimization carried out on several typical railway trusses. The influences of buckling, deformation constraints, and the optimum height of each type of truss, assessed using a simple approximation method.

Development of Self-Adaptive Meta-Heuristic Optimization Algorithm: Self-Adaptive Vision Correction Algorithm (자가 적응형 메타휴리스틱 최적화 알고리즘 개발: Self-Adaptive Vision Correction Algorithm)

  • Lee, Eui Hoon;Lee, Ho Min;Choi, Young Hwan;Kim, Joong Hoon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.6
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    • pp.314-321
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    • 2019
  • The Self-Adaptive Vision Correction Algorithm (SAVCA) developed in this study was suggested for improving usability by modifying four parameters (Modulation Transfer Function Rate, Astigmatic Rate, Astigmatic Factor and Compression Factor) except for Division Rate 1 and Division Rate 2 among six parameters in Vision Correction Algorithm (VCA). For verification, SAVCA was applied to two-dimensional mathematical benchmark functions (Six hump camel back / Easton and fenton) and 30-dimensional mathematical benchmark functions (Schwefel / Hyper sphere). It showed superior performance to other algorithms (Harmony Search, Water Cycle Algorithm, VCA, Genetic Algorithms with Floating-point representation, Shuffled Complex Evolution algorithm and Modified Shuffled Complex Evolution). Finally, SAVCA showed the best results in the engineering problem (speed reducer design). SAVCA, which has not been subjected to complicated parameter adjustment procedures, will be applicable in various fields.