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Complete receptor editing operation based on quantum clonal selection algorithm for optimization problems
Author(s) -
Yang Yu,
Dai Hongwei,
Gao Shangce,
Wang Yirui,
Jia Dongbao,
Tang Zheng
Publication year - 2019
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22822
Subject(s) - clonal selection , clonal selection algorithm , travelling salesman problem , selection (genetic algorithm) , somatic evolution in cancer , artificial immune system , crossover , computer science , selection algorithm , biology , computational biology , clonal deletion , algorithm , immune system , t cell receptor , genetics , artificial intelligence , t cell , immunology , gene
Clonal selection mechanism, which is the theoretical foundation of clonal selection algorithm (CSA) and its variants, was proposed for explaining the essential features of adaptive immune responses: adequate diversity, discrimination of self and nonself, and sustaining immunologic memory. On the basis of the clonal selection theory, only the high‐affinity immune cells are chosen to proliferate. Those cells with low affinity must be efficiently eliminated. However, the ability of receptor editing to salvage low‐affinity immune cells from deletion by changing their receptor specificity realized the clonal selection process anew. By combining clonal selection theory and receptor editing, a quantum clonal selection algorithm based on complete receptor editing operation is proposed for the traveling salesman problem (TSP) and the holes‐machining‐path‐planning (HMPP) problem. Two receptor editing operators (inversion and deletion) work together to improve the performance of CSA. Furthermore, in order to overcome the drawback of asexual proliferation during the immune maturation process, a quantum interference crossover based on complete receptor editing operation is used. The effectiveness of the improved algorithm is evaluated on optimization problems including TSP and HMPP problems. The experimental results are also compared with those of other methods based on clonal selection theory. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.