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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.9
- 수록면
- 223 - 233 (11page)
- DOI
- 10.7315/CDE.2017.223
이용수
초록· 키워드
오류제보하기
Applications of global optimization techniques such as metaheuristics in engineering are recently reported in a lot of literatures. The problems to tackle are constrained optimization problems and are hence hard to solve. Furthermore, the metaheuristic algorithms have several to many algorithmic parameters to be set by a user. Engineers have very little domain knowledge of their problems and setting of the algorithmic parameters is a difficult task. One remedy for this trouble is a development of parameter-less algorithm. One of such algorithms developed recently is Jaya and it shows a prospective performance with a simple single-step method. This study proposes a combined algorithm to adopt Jaya, augment capability of handling constraints, and enhance its performance. The constraint handling should also be parameter-less and algorithmic enhancement is achieved by adding a differential evolution technique. The proposed algorithm has been applied to five engineering design problems. The proposed method shows comparable in convergence and superior performance in robustness to the original method.
목차
ABSTRACT
1. 서론
2. Jaya알고리즘의 개선
3. 구속 조건의 처리 방법
4. 성능 평가 및 수치 실험
5. 결론
References
참고문헌 (17)
참고문헌 신청
Beheshti, Z. and Shamsuddin, S., 2013, A Review of Population-based Meta-Heuristic Algorithms, Int. J. Adv. Soft Comput Appl, 5(1), pp.1-35.
Coello Coello, C.A., 2010, Constraint-Handling Techniques used with Evolutionary Algorithms, In Proceedings of the Genetic and Evolutionary Computation Conference, pp.2603-2624.
Deb, K., 2000, An Efficient Constraint Handling Method for Genetic Algorithms, Computer Methods in Applied Mechanics and Engineering, 186(2-4), pp.311-338.
Dong Li, Jie Chen and Bin Xin., 2013, A Novel Differential Evolution Algorithm with Gaussian Mutation that Balances Exploration and Exploitation, In Differential Evolution (SDE), 2013 IEEE Symposium, pp.18-24.
Jordehi, A.R., 2015, A Review on Constraint Handling Strategies in Particle Swarm Optimisation, Neural Computing and Applications, 26(6), pp.1-11.
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UCI(KEPA) : I410-ECN-0101-2018-530-001167468