期刊
FRONTIERS OF COMPUTER SCIENCE
卷 8, 期 4, 页码 642-655出版社
HIGHER EDUCATION PRESS
DOI: 10.1007/s11704-014-3093-y
关键词
many-objective optimization; preference multi-objective optimization; artificial immune system; reference direction method; light beam search; intelligent recombination operator
类别
资金
- National Natural Science Foundation of China [61373111, 61272279, 61003199, 61203303]
- Fundamental Research Funds for the Central Universities [K50511020014, K5051302084, K50510020011, K5051302049, K5051302023]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
- Program for New Century Excellent Talents in University [NCET-12-0920]
In this paper, a new preference multi-objective optimization algorithm called immune clone algorithm based on reference direction method (RD-ICA) is proposed for solving many-objective optimization problems. First, an intelligent recombination operator, which performs well on the functions comprising many parameters, is introduced into an immune clone algorithm so as to explore the potentially excellent gene segments of all individuals in the antibody population. Second, a reference direction method, a very strict ranking based on the desire of decision makers (DMs), is used to guide selection and clone of the active population. Then a light beam search (LBS) is borrowed to pick out a small set of individuals filling the external population. The proposed method has been extensively compared with other recently proposed evolutionary multi-objective optimization (EMO) approaches over DTLZ problems with from 4 to 100 objectives. Experimental results indicate RD-ICA can achieve competitive results.
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