An adaptive immune-inspired multi-objective algorithm with multiple differential evolution strategies
Most multi-objective immune algorithms (MOIAs) adopt clonal selection to speed up convergence, as this operator only clones the best individuals during the search process. However, this approach somehow deteriorates the population diversity, which may cause a MOIA to be trapped in a local optimum an...
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| Published in | Information sciences Vol. 430-431; pp. 46 - 64 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.03.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 |
| DOI | 10.1016/j.ins.2017.11.030 |
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| Abstract | Most multi-objective immune algorithms (MOIAs) adopt clonal selection to speed up convergence, as this operator only clones the best individuals during the search process. However, this approach somehow deteriorates the population diversity, which may cause a MOIA to be trapped in a local optimum and could also lead to premature convergence when tackling some complicated multi-objective optimization problems (MOPs). In order to overcome this problem, an adaptive immune-inspired multi-objective algorithm (AIMA) is presented in this paper, in which multiple differential evolution (DE) strategies having distinct advantages are embedded into a conventional MOIA. Our proposed approach strengthens the exploration capabilities of a MOIA while also improving its population diversity. At each generation, based on the current search stage, an adaptive selection method is designed to choose an appropriate DE strategy for evolution. The core idea is to effectively combine the advantages of three DE strategies when solving different MOPs. A number of comparative experiments are conducted on the well-known and frequently-used WFG and DTLZ test problems. Our experimental results validate the superiority of our proposed AIMA, as it performs better than some state-of-the-art multi-objective optimization algorithms and some state-of-the-art MOIAs. |
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| AbstractList | Most multi-objective immune algorithms (MOIAs) adopt clonal selection to speed up convergence, as this operator only clones the best individuals during the search process. However, this approach somehow deteriorates the population diversity, which may cause a MOIA to be trapped in a local optimum and could also lead to premature convergence when tackling some complicated multi-objective optimization problems (MOPs). In order to overcome this problem, an adaptive immune-inspired multi-objective algorithm (AIMA) is presented in this paper, in which multiple differential evolution (DE) strategies having distinct advantages are embedded into a conventional MOIA. Our proposed approach strengthens the exploration capabilities of a MOIA while also improving its population diversity. At each generation, based on the current search stage, an adaptive selection method is designed to choose an appropriate DE strategy for evolution. The core idea is to effectively combine the advantages of three DE strategies when solving different MOPs. A number of comparative experiments are conducted on the well-known and frequently-used WFG and DTLZ test problems. Our experimental results validate the superiority of our proposed AIMA, as it performs better than some state-of-the-art multi-objective optimization algorithms and some state-of-the-art MOIAs. |
| Author | Chen, Fei Ma, Yueping Wong, Ka-Chun Coello, Carlos A. Coello Chen, Jianyong Zhu, Qingling Lin, Qiuzhen |
| Author_xml | – sequence: 1 givenname: Qiuzhen surname: Lin fullname: Lin, Qiuzhen organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 2 givenname: Yueping surname: Ma fullname: Ma, Yueping organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 3 givenname: Jianyong surname: Chen fullname: Chen, Jianyong organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 4 givenname: Qingling surname: Zhu fullname: Zhu, Qingling organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 5 givenname: Carlos A. Coello surname: Coello fullname: Coello, Carlos A. Coello organization: CINVESTAV-IPN, Department of Computer Science, Mexico, D.F. 07360, Mexico – sequence: 6 givenname: Ka-Chun surname: Wong fullname: Wong, Ka-Chun organization: Department of Computer Science, City University of Hong Kong, Hong Kong, PR China – sequence: 7 givenname: Fei surname: Chen fullname: Chen, Fei email: fchen@szu.edu.cn organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China |
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