TY - GEN
T1 - Hierarchical Water Wave Optimization
AU - Dong, Shibo
AU - Yang, Haichuan
AU - Li, Haotian
AU - Zhang, Baohang
AU - Tao, Sichen
AU - Gao, Shangce
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Water wave optimization algorithm (WWO) draws inspiration from the natural summary of the shallow water wave theory. It benefits from a modest population size and straightforward parameter design. However, WWO still has some performance problems that need to be solved, e.g., the convergence speed is too slow, and it cannot find the optimal point efficiently and accurately. This paper proposes a strategy of multi-level population structure for it, namely DWWO. The multi-level population structure strategy further enhances the balance between exploitation performance and exploration performance of the WWO algorithm. It makes the algorithm performance more stable, which leads to the DWWO algorithm can be used in more practical problems. DWWO algorithm is compared with the classical WWO algorithm, cuckoo search algorithm, sparrow search algorithm, and sine cosine algorithm on the basis of IEEE CEC2017 problem set. Comprehensive experimental results show that DWWO algorithm has better optimization ability and relatively fast convergence speed in comparison with other algorithms.
AB - Water wave optimization algorithm (WWO) draws inspiration from the natural summary of the shallow water wave theory. It benefits from a modest population size and straightforward parameter design. However, WWO still has some performance problems that need to be solved, e.g., the convergence speed is too slow, and it cannot find the optimal point efficiently and accurately. This paper proposes a strategy of multi-level population structure for it, namely DWWO. The multi-level population structure strategy further enhances the balance between exploitation performance and exploration performance of the WWO algorithm. It makes the algorithm performance more stable, which leads to the DWWO algorithm can be used in more practical problems. DWWO algorithm is compared with the classical WWO algorithm, cuckoo search algorithm, sparrow search algorithm, and sine cosine algorithm on the basis of IEEE CEC2017 problem set. Comprehensive experimental results show that DWWO algorithm has better optimization ability and relatively fast convergence speed in comparison with other algorithms.
KW - Exploration and exploitation
KW - Hierar-chical
KW - Meta-heuristic algorithms
KW - Population structure
KW - Water Wave Optimization
UR - http://www.scopus.com/inward/record.url?scp=85146922851&partnerID=8YFLogxK
U2 - 10.1109/ICNSC55942.2022.10004174
DO - 10.1109/ICNSC55942.2022.10004174
M3 - 会議への寄与
AN - SCOPUS:85146922851
T3 - ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control: Autonomous Intelligent Systems
BT - ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022
Y2 - 15 December 2022 through 18 December 2022
ER -