TY - JOUR
T1 - Local Binary Pattern-Based Adaptive Differential Evolution for Multimodal Optimization Problems
AU - ZHAO, Hong
AU - ZHAN, Zhi-Hui
AU - LIN, Ying
AU - CHEN, Xiaofeng
AU - LUO, Xiao-Nan
AU - ZHANG, Jie
AU - KWONG, Sam
AU - ZHANG, Jun
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - The multimodal optimization problem (MMOP) requires the algorithm to find multiple global optima of the problem simultaneously. In order to solve MMOP efficiently, a novel differential evolution (DE) algorithm based on the local binary pattern (LBP) is proposed in this paper. The LBP makes use of the neighbors' information for extracting relevant pattern information, so as to identify the multiple regions of interests, which is similar to finding multiple peaks in MMOP. Inspired by the principle of LBP, this paper proposes an LBP-based adaptive DE (LBPADE) algorithm. It enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP. Moreover, based on the LBP niching information, we develop a niching and global interaction (NGI) mutation strategy and an adaptive parameter strategy (APS) to fully search the niching areas and maintain multiple peak regions. The proposed NGI mutation strategy incorporates information from both the niching and the global areas for effective exploration, while APS adjusts the parameters of each individual based on its own LBP information and guides the individual to the promising direction. The proposed LBPADE algorithm is evaluated on the extensive MMOPs test functions. The experimental results show that LBPADE outperforms or at least remains competitive with some state-of-the-art algorithms.
AB - The multimodal optimization problem (MMOP) requires the algorithm to find multiple global optima of the problem simultaneously. In order to solve MMOP efficiently, a novel differential evolution (DE) algorithm based on the local binary pattern (LBP) is proposed in this paper. The LBP makes use of the neighbors' information for extracting relevant pattern information, so as to identify the multiple regions of interests, which is similar to finding multiple peaks in MMOP. Inspired by the principle of LBP, this paper proposes an LBP-based adaptive DE (LBPADE) algorithm. It enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP. Moreover, based on the LBP niching information, we develop a niching and global interaction (NGI) mutation strategy and an adaptive parameter strategy (APS) to fully search the niching areas and maintain multiple peak regions. The proposed NGI mutation strategy incorporates information from both the niching and the global areas for effective exploration, while APS adjusts the parameters of each individual based on its own LBP information and guides the individual to the promising direction. The proposed LBPADE algorithm is evaluated on the extensive MMOPs test functions. The experimental results show that LBPADE outperforms or at least remains competitive with some state-of-the-art algorithms.
KW - Adaptive differential evolution (DE)
KW - DE
KW - local binary pattern (LBP) strategy
KW - multimodal optimization problems (MMOPs)
UR - http://www.scopus.com/inward/record.url?scp=85086749848&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2019.2927780
DO - 10.1109/TCYB.2019.2927780
M3 - Journal Article (refereed)
SN - 2168-2267
VL - 50
SP - 3343
EP - 3357
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 7
M1 - 8792370
ER -