TY - GEN
T1 - A Covariance Matrix Adaptation Evolution Strategy-based Manta Ray Foraging optimization
AU - Li, Qianqian
AU - Liu, Tongyan
AU - He, Houtian
AU - Wang, Kaiyu
AU - Gao, Shangce
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Covariance matrix adaptive evolution strategy (CMAES) is an excellent local search strategy that does not rely on gradient information. It is widely used in real-valued optimization because it fast converges to the local optimum. In addition, manta ray foraging optimization (MRFO) is an evolutionary algorithm inspired by the manta rays' three particular foraging strategies which can carry out a good solution for global optimization problems. In this paper, we use CMAES to strengthen the local search ability of MRFO and propose CMAES-MRFO to generate better solutions. The CEC'2017 benchmark functions are employed to test the hybrid algorithm. Experimental results show that the proposed CMAES-MRFO significantly outperforms its original algorithms and some other newly-coming ones.
AB - Covariance matrix adaptive evolution strategy (CMAES) is an excellent local search strategy that does not rely on gradient information. It is widely used in real-valued optimization because it fast converges to the local optimum. In addition, manta ray foraging optimization (MRFO) is an evolutionary algorithm inspired by the manta rays' three particular foraging strategies which can carry out a good solution for global optimization problems. In this paper, we use CMAES to strengthen the local search ability of MRFO and propose CMAES-MRFO to generate better solutions. The CEC'2017 benchmark functions are employed to test the hybrid algorithm. Experimental results show that the proposed CMAES-MRFO significantly outperforms its original algorithms and some other newly-coming ones.
KW - Manta ray foraging optimization
KW - covariance matrix adaptation evolution strategy
KW - evolutionary algorithms
KW - exploration and exploitation
UR - http://www.scopus.com/inward/record.url?scp=85146688368&partnerID=8YFLogxK
U2 - 10.1109/SCISISIS55246.2022.10001965
DO - 10.1109/SCISISIS55246.2022.10001965
M3 - 会議への寄与
AN - SCOPUS:85146688368
T3 - 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022
BT - 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022
Y2 - 29 November 2022 through 2 December 2022
ER -