TY - JOUR
T1 - An adaptive clustering-based evolutionary algorithm for many-objective optimization problems
AU - Liu, Songbai
AU - Yu, Qiyuan
AU - Lin, Qiuzhen
AU - Tan, Kay Chen
N1 - Funding Information:
This work was supported by the Shenzhen Technology Plan under Grant JCYJ20190808164211203 , the National Natural Science Foundation of China (NSFC) under Grants 61876110 , the Natural Science Foundation of Guangdong Province under Grants 2017A030313338 , and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University . This work was also supported in part by the NSFC under grant 61876162, by the Shenzhen Scientific Research and Development Funding Program under grant JCYJ20180307123637294 , and by the Research Grants Council of the Hong Kong SAR under grant No. CityU11202418 and CityU11209219 .
Publisher Copyright:
© 2020
PY - 2020/10
Y1 - 2020/10
N2 - This paper proposes an adaptive clustering-based evolutionary algorithm for many-objective optimization problems (MaOPs), called MaOEA/AC. In this algorithm, an adaptive clustering strategy (ACS) is first introduced to divide the population into multiple clusters, which can properly fit various Pareto fronts (PFs) of the target MaOPs. Then, the environmental selection of MaOEA/AC is designed based on these clusters to collect the solutions with balanceable convergence and diversity. To be more detail, the similarity between solutions in ACS is appropriately measured by computing the Euclidean distance between their projections on an adaptive unit hyper-surface, whose curving rate is controlled by a parameter p. A simple yet effective estimation method is proposed to get a suitable value of p based on the distribution of the current non-dominated solution set, so that the estimated unit hyper-surface can roughly reflect the characteristics of PFs in the target MaOPs. The effectiveness of MaOEA/AC is validated by numerous experimental studies on solving test MaOPs with various PFs, which have the characteristics with convex, concave, inverted, disconnected, degenerated, and other mixed or irregular PFs. The experiments also show that MaOEA/AC has the superior performance over several recent many-objective evolutionary algorithms, when solving most of these test MaOPs.
AB - This paper proposes an adaptive clustering-based evolutionary algorithm for many-objective optimization problems (MaOPs), called MaOEA/AC. In this algorithm, an adaptive clustering strategy (ACS) is first introduced to divide the population into multiple clusters, which can properly fit various Pareto fronts (PFs) of the target MaOPs. Then, the environmental selection of MaOEA/AC is designed based on these clusters to collect the solutions with balanceable convergence and diversity. To be more detail, the similarity between solutions in ACS is appropriately measured by computing the Euclidean distance between their projections on an adaptive unit hyper-surface, whose curving rate is controlled by a parameter p. A simple yet effective estimation method is proposed to get a suitable value of p based on the distribution of the current non-dominated solution set, so that the estimated unit hyper-surface can roughly reflect the characteristics of PFs in the target MaOPs. The effectiveness of MaOEA/AC is validated by numerous experimental studies on solving test MaOPs with various PFs, which have the characteristics with convex, concave, inverted, disconnected, degenerated, and other mixed or irregular PFs. The experiments also show that MaOEA/AC has the superior performance over several recent many-objective evolutionary algorithms, when solving most of these test MaOPs.
KW - Adaptive clustering
KW - Evolutionary algorithm
KW - Many-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85086375411&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2020.03.104
DO - 10.1016/j.ins.2020.03.104
M3 - Journal article
AN - SCOPUS:85086375411
SN - 0020-0255
VL - 537
SP - 261
EP - 283
JO - Information Sciences
JF - Information Sciences
ER -