TY - CHAP
T1 - Development of Deep Learning-based Self-adaptive Harmony Search
AU - Kim, Taewook
AU - Jung, Hyeon Woo
AU - Kim, Joong Hoon
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Nature-inspired optimization algorithms are widely used in various mathematical and engineering problems because of their usability and applicability. However, these optimization algorithms show different performances depending on the characteristics of the problem applied. There have been various effort to solve this problem by developing a new algorithm, applying other heuristics, changing parameters, etc. The deep learning-based self-adaptive harmony search (DLSaHS) developed in this study is another effort to tackle the problem by controlling the probability of heuristics by using recurrent neural network (RNN) and the parameter called checkpoint (CP). DLSaHS contains the heuristics obtained from harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), and copycat harmony search (CcHS). DLSaHS was applied to the ten mathematical benchmark problems obtained from IEEE CEC 2021. The performance of DLSaHS is compared to the HS which showed better performance. Also, the optimal CP value obtained when applied to low-dimensional problems and the probability of heuristics according to the CP were derived to efficiently apply the DLSaHS, and it is confirmed that the error and standard deviation of the result, and computation time can be considerably reduced by applying them to high-dimensional problems.
AB - Nature-inspired optimization algorithms are widely used in various mathematical and engineering problems because of their usability and applicability. However, these optimization algorithms show different performances depending on the characteristics of the problem applied. There have been various effort to solve this problem by developing a new algorithm, applying other heuristics, changing parameters, etc. The deep learning-based self-adaptive harmony search (DLSaHS) developed in this study is another effort to tackle the problem by controlling the probability of heuristics by using recurrent neural network (RNN) and the parameter called checkpoint (CP). DLSaHS contains the heuristics obtained from harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), and copycat harmony search (CcHS). DLSaHS was applied to the ten mathematical benchmark problems obtained from IEEE CEC 2021. The performance of DLSaHS is compared to the HS which showed better performance. Also, the optimal CP value obtained when applied to low-dimensional problems and the probability of heuristics according to the CP were derived to efficiently apply the DLSaHS, and it is confirmed that the error and standard deviation of the result, and computation time can be considerably reduced by applying them to high-dimensional problems.
KW - CEC 2021 benchmark functions
KW - Deep learning-based
KW - DLSaHS
KW - Probability of heuristics
KW - RNN
UR - http://www.scopus.com/inward/record.url?scp=85137564536&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-2948-9_33
DO - 10.1007/978-981-19-2948-9_33
M3 - Chapter
AN - SCOPUS:85137564536
T3 - Lecture Notes on Data Engineering and Communications Technologies
SP - 345
EP - 352
BT - Lecture Notes on Data Engineering and Communications Technologies
PB - Springer Science and Business Media Deutschland GmbH
ER -