한국인터넷정보학회 논문지
Current Result Document :
| 한글제목(Korean Title) |
A Hybrid Estimation of Distribution Algorithm with Differential Evolution based on Self-adaptive Strategy |
| 영문제목(English Title) |
A Hybrid Estimation of Distribution Algorithm with Differential Evolution based on Self-adaptive Strategy |
| 저자(Author) |
Debin Fan
Jaewan Lee
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| 원문수록처(Citation) |
VOL 22 NO. 01 PP. 0001 ~ 0011 (2021. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.
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| 키워드(Keyword) |
Hybrid algorithm
Estimation of Distribution
Differential Evolution
Self-adaptive Strategy
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| 파일첨부 |
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