Enhancing Modified Cuckoo Search Algorithm by Using MCMC Random Walk
| Main Authors: | Aida Husaini, Noor, Ghazali, Rozaida, Tri Riyadi Yanto, Iwan |
|---|---|
| Format: | Article PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
https://ieeexplore.ieee.org/document/7852653
, 2018
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| Subjects: | |
| Online Access: |
http://eprints.uad.ac.id/11649/1/Enhancing%20Modified%20Cuckoo%20Search%20Algorithm%20by%20Using%20MCMC%20Random%20Walk.pdf http://eprints.uad.ac.id/11649/ |
Daftar Isi:
- In this paper, we scrutinised an improvement of the Modified Cuckoo Search (MCS), called Modified Cuckoo Searoh-Markov chain Monte Carlo (MCS-MCMC) algorithm, for solving optimisation problems. The performance of MCS are at least on a par with the standard Cuckoo Search (CS) in terms of high rate of convergence when dealing with true global minimum, although at high number of dimensions. In conjunction with the benefits of MCS, we aim to enhance the MCS algorithm by applying Markov chain Monte Carlo (MCMC) random walk. We validated the proposed algorithm alongside several test functions and later on, we compare its performance with those of MCS-Levy algorithm. The capability of the MCS-MCMC algorithm in yielding good results is considered as a solution to deal with the downside of those existing algorithm Keyword&- MCS-MCMC, modified cuckoo search, cuckoo search, Markov chain Monte Carlo