TY - GEN
T1 - An Immigration Strategy-based Spherical Search Algorithm
AU - Sui, Qingya
AU - Tao, Sichen
AU - Zhong, Lin
AU - Yang, Haichuan
AU - Lei, Zhenyu
AU - Gao, Shangce
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The spherical search algorithm (SS) is a novel and competitive algorithm applied to real-world problems. However, the population of SS algorithm is divided equally, which requires a large number of computation resources for different problems. To alleviate the issues, we propose an immigration strategy-based spherical search algorithm, namely ISS. ISS adaptively selects individuals that are successfully updated in each generation and replaces the operator in the next iteration. The experiments were conducted on the 30 benchmark functions from the IEEE CEC2017. ISS is compared with SS to verify the effectiveness of the proposed adaptive immigration strategy. Additionally, the classical differential evolutionary algorithm (DE) and a state-of-the-art triple archive particle swarm optimization (TAPSO) are compared to test its performance further. The population diversity is analyzed to discuss the effect of ISS. The experimental results demonstrate that the proposed immigration strategy is quite effective, and ISS is significantly better than its peer's algorithms.
AB - The spherical search algorithm (SS) is a novel and competitive algorithm applied to real-world problems. However, the population of SS algorithm is divided equally, which requires a large number of computation resources for different problems. To alleviate the issues, we propose an immigration strategy-based spherical search algorithm, namely ISS. ISS adaptively selects individuals that are successfully updated in each generation and replaces the operator in the next iteration. The experiments were conducted on the 30 benchmark functions from the IEEE CEC2017. ISS is compared with SS to verify the effectiveness of the proposed adaptive immigration strategy. Additionally, the classical differential evolutionary algorithm (DE) and a state-of-the-art triple archive particle swarm optimization (TAPSO) are compared to test its performance further. The population diversity is analyzed to discuss the effect of ISS. The experimental results demonstrate that the proposed immigration strategy is quite effective, and ISS is significantly better than its peer's algorithms.
KW - Immigration Strategy
KW - Meta-heuristic algorithms
KW - Population structure
KW - Spherical Search algorithm optimization
UR - http://www.scopus.com/inward/record.url?scp=85146935125&partnerID=8YFLogxK
U2 - 10.1109/ICNSC55942.2022.10004149
DO - 10.1109/ICNSC55942.2022.10004149
M3 - 会議への寄与
AN - SCOPUS:85146935125
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 -
