TY - GEN
T1 - A Population Resource Allocation-based Adaptive Spherical Search Algorithm
AU - Zhong, Lin
AU - Tao, Sichen
AU - Sui, Qingya
AU - Yang, Haichuan
AU - Lei, Zhenyu
AU - Gao, Shangce
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The spherical search (SS) algorithm is a meta-heuristic algorithm which aims to solve the global optimization problems that is non-linear and bound-constrained. Due to the balance between the exploration and exploitation capabilities of the algorithm by utilizing two mechanisms, namely, toward-random and toward-best, the SS algorithm shows superior performance compared to some other meta-heuristic techniques. However, the algorithm still suffers from the drawback of being easily trapped in local optima. In this paper, we propose a population resource allocation-based adaptive spherical search algorithm that adaptively adjusts the ratio of population resource allocation. The algorithm evaluates the performance of different operators at the current search stage in real-time during the iteration, and continuously adjusts the resource allocation ratio based on the performance until the end of the iteration. Experimental results are obtained on the basis of IEEE Congress on Evolutionary Computation (CEC) 2017 problem set to verify the effectiveness and the efficiency of the proposed method.
AB - The spherical search (SS) algorithm is a meta-heuristic algorithm which aims to solve the global optimization problems that is non-linear and bound-constrained. Due to the balance between the exploration and exploitation capabilities of the algorithm by utilizing two mechanisms, namely, toward-random and toward-best, the SS algorithm shows superior performance compared to some other meta-heuristic techniques. However, the algorithm still suffers from the drawback of being easily trapped in local optima. In this paper, we propose a population resource allocation-based adaptive spherical search algorithm that adaptively adjusts the ratio of population resource allocation. The algorithm evaluates the performance of different operators at the current search stage in real-time during the iteration, and continuously adjusts the resource allocation ratio based on the performance until the end of the iteration. Experimental results are obtained on the basis of IEEE Congress on Evolutionary Computation (CEC) 2017 problem set to verify the effectiveness and the efficiency of the proposed method.
KW - Exploration and exploitation
KW - Meta-heuristic algorithm
KW - Population resource allocation
KW - Spherical search
UR - http://www.scopus.com/inward/record.url?scp=85146918682&partnerID=8YFLogxK
U2 - 10.1109/ICNSC55942.2022.10004116
DO - 10.1109/ICNSC55942.2022.10004116
M3 - 会議への寄与
AN - SCOPUS:85146918682
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 -