Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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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 (School of Civil Engineering, Chungbuk National University) ;
  • Lee, Ho Min (Research Center for Disaster Prevention Science and Technology, Korea University) ;
  • Choi, Young Hwan (Research Center for Disaster Prevention Science and Technology, Korea University) ;
  • Kim, Joong Hoon (School of Civil, Environmental, and Architectural Engineering, Korea University)
  • 이의훈 (충북대학교 토목공학부) ;
  • 이호민 (고려대학교 방재과학기술연구) ;
  • 최영환 (고려대학교 방재과학기술연구) ;
  • 김중훈 (고려대학교 건축사회환경공학부)
  • Received : 2019.03.19
  • Accepted : 2019.06.07
  • Published : 2019.06.30
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Abstract

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.

본 연구에서 개발된 Self-Adaptive Vision Correction Algorithm (SAVCA)은 광학적 특성을 모방하여 개발된 Vision Correction Algorithm (VCA)의 총 6개의 매개변수 중 자가 적응형태로 구축된 Division Rate 1 (DR1) 및 Division Rate 2 (DR2)를 제외한 Modulation Transfer Function Rate (MR), Astigmatic Rate (AR), Astigmatic Factor (AF) 및 Compression Factor (CF) 등 4개의 매개변수를 변경하여 사용성을 증대시키기 위해 제시되었다. 개발된 SAVCA의 검증을 위해 기존 VCA를 적용하였던 2개 변수를 갖는 수학 문제 (Six hump camel back 및 Easton and fenton) 및 30개 변수를 갖는 수학 문제 (Schwefel 및 Hyper sphere)에 적용한 결과 SAVCA는 비교한 다른 알고리즘 (Harmony Search, Water Cycle Algorithm, VCA, Genetic Algorithms with Floating-point representation, Shuffled Complex Evolution algorithm 및 Modified Shuffled Complex Evolution)에 비해 우수한 성능을 보여주었다. 마지막으로 공학 문제인 Speed reducer design에서도 SAVCA는 가장 좋은 결과를 보여주었다. 복잡한 매개변수 조절과정을 거치지 않은 SAVCA는 여러 분야에서 적용이 가능할 것이다.

Keywords

Table 1. Comparison for each operator in VCA and SAVCA

SHGSCZ_2019_v20n6_314_t0001.png 이미지

Table 2. Pseudo code of SAVCA

SHGSCZ_2019_v20n6_314_t0002.png 이미지

Table 3. Specification of problems for comparison [1]

SHGSCZ_2019_v20n6_314_t0003.png 이미지

Table 4. Results for application of 2D mathematical benchmark function (Six Hump Camel Back) [1]

SHGSCZ_2019_v20n6_314_t0004.png 이미지

Table 5. Results for application of 2D mathematical benchmark function (Easton and Fenton) [1]

SHGSCZ_2019_v20n6_314_t0005.png 이미지

Table 7. Results for application of 30D mathematical benchmark function (Hyper Sphere) [1]

SHGSCZ_2019_v20n6_314_t0006.png 이미지

Table 8. Results for application of engineering function (Speed reducer design) [1]

SHGSCZ_2019_v20n6_314_t0007.png 이미지

Table 6. Results for application of 30D mathematical benchmark function (Schwefel) [1]

SHGSCZ_2019_v20n6_314_t0008.png 이미지

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