TY - GEN
T1 - Archive-based Differential Learning Incorporated Sparrow Search Algorithm
AU - Yu, Qianrui
AU - Wang, Ziqian
AU - Li, Haotian
AU - Yang, Yifei
AU - Lei, Zhenyu
AU - Gao, Shangce
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Sparrow search algorithm (SSA) is a new evolutionary algorithm that has the advantage of good exploration ability. Benefiting from its search strategy, SSA can effectively alleviate the local optima. However, it still suffers from the issue of low solution quality because of its weak exploitation ability. Therefore, we propose an archive-based differential learning incorporated sparrow search algorithm (ASSA), which introduces a local search strategy for enhancing the exploitation ability of SSA. The local search strategy searches for a superior solution in the neighborhood region of the optimal solution in each iteration and uses it to replace the original optimal solution so that SSA can find a better solution. ASSA is compared with four state-of-the-art algorithms on 29 benchmark functions of IEEE CEC2017. The experiment results demonstrate that ASSA has superior performance to its competitors.
AB - Sparrow search algorithm (SSA) is a new evolutionary algorithm that has the advantage of good exploration ability. Benefiting from its search strategy, SSA can effectively alleviate the local optima. However, it still suffers from the issue of low solution quality because of its weak exploitation ability. Therefore, we propose an archive-based differential learning incorporated sparrow search algorithm (ASSA), which introduces a local search strategy for enhancing the exploitation ability of SSA. The local search strategy searches for a superior solution in the neighborhood region of the optimal solution in each iteration and uses it to replace the original optimal solution so that SSA can find a better solution. ASSA is compared with four state-of-the-art algorithms on 29 benchmark functions of IEEE CEC2017. The experiment results demonstrate that ASSA has superior performance to its competitors.
KW - Exploration and exploitation
KW - Local search
KW - Meta-heuristic algorithms
KW - Sparrow search algorithm
UR - http://www.scopus.com/inward/record.url?scp=85147854012&partnerID=8YFLogxK
U2 - 10.1109/ISCID56505.2022.00054
DO - 10.1109/ISCID56505.2022.00054
M3 - 会議への寄与
AN - SCOPUS:85147854012
T3 - Proceedings - 2022 15th International Symposium on Computational Intelligence and Design, ISCID 2022
SP - 212
EP - 216
BT - Proceedings - 2022 15th International Symposium on Computational Intelligence and Design, ISCID 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Symposium on Computational Intelligence and Design, ISCID 2022
Y2 - 17 December 2022 through 18 December 2022
ER -