Multiobjective optimization using an immunodominance and clonal selection inspired algorithm |
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基金项目: | Supported by the National Natural Science Foundation of China (Grant Nos. 60703107 and 60703108), the National High Technology Research and Development Program (863 Program) of China (Grant No. 2006AA01Z107), the National Basic Research Program (973 Program) of China (Grant No. 2006CB705700) and the Program for Cheung Kong Scholars and Innovative Research Team in University (Grant No. IRT0645) |
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摘 要: | Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.
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关 键 词: | 多目标优化 免疫优势 无性繁殖选择 人造免疫系统 |
Multiobjective optimization using an immunodominance and clonal selection inspired algorithm |
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Authors: | MaoGuo Gong LiCheng Jiao WenPing Ma and HaiFeng Du |
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Affiliation: | (1) Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071, China;(2) School of Public and Administration, Xi’an Jiaotong University, Xi’an, 710049, China |
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Abstract: | Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm,
immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated
individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals
and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals
(antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies
to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they
can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation
is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their
improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and
the newcomers yield a broader exploration of the search space. The performan ce comparison of IDCMA with MISA, NSGA-II, SPEA,
PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective
0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good
distribution.
Supported by the National Natural Science Foundation of China (Grant Nos. 60703107 and 60703108), the National High Technology
Research and Development Program (863 Program) of China (Grant No. 2006AA01Z107), the National Basic Research Program (973
Program) of China (Grant No. 2006CB705700) and the Program for Cheung Kong Scholars and Innovative Research Team in University
(Grant No. IRT0645) |
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Keywords: | multiobjective optimization immunodominance clonal selection artificial immune systems evolutionary algorithms |
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