A survey of artificial immune algorithms for multi-objective optimization

Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, artificial immune algorithm has become a research hotspot in the fi...

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Vydané v:Neurocomputing (Amsterdam) Ročník 489; s. 211 - 229
Hlavní autori: Li, Lingjie, Lin, Qiuzhen, Ming, Zhong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 07.06.2022
Predmet:
Artificial immune algorithm
Clonal selection
Constrained optimization
Dynamic optimization
Multi-objective optimization
Clonal selection
Multi-objective optimization
Artificial immune algorithm
Dynamic optimization
Constrained optimization
ISSN:0925-2312, 1872-8286
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Abstract Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, artificial immune algorithm has become a research hotspot in the field of multi-objective optimization after the evolutionary algorithms. In this paper, most MOIAs can be classified into three main categories according to the type of problem solving, i.e., they are mostly designed to solve multi-objective optimization problems (MOPs), dynamic MOPs, and constrained MOPs. In this paper, a comprehensive survey is presented to summarize most existing MOIAs, in which their corresponding characteristics, principles and theoretical analyses are discussed in details. Moreover, the performance of MOIAs on solving various kinds of MOPs and many-objective optimization problems is also studied in our experimental comparisons. Finally, a brief conclusion is given to summarize the current drawbacks, challenges, and some future directions for MOIAs.
AbstractList Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, artificial immune algorithm has become a research hotspot in the field of multi-objective optimization after the evolutionary algorithms. In this paper, most MOIAs can be classified into three main categories according to the type of problem solving, i.e., they are mostly designed to solve multi-objective optimization problems (MOPs), dynamic MOPs, and constrained MOPs. In this paper, a comprehensive survey is presented to summarize most existing MOIAs, in which their corresponding characteristics, principles and theoretical analyses are discussed in details. Moreover, the performance of MOIAs on solving various kinds of MOPs and many-objective optimization problems is also studied in our experimental comparisons. Finally, a brief conclusion is given to summarize the current drawbacks, challenges, and some future directions for MOIAs.
Author Li, Lingjie
Ming, Zhong
Lin, Qiuzhen
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  email: qiuzhlin@szu.edu.cn
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  givenname: Zhong
  surname: Ming
  fullname: Ming, Zhong
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Keywords Clonal selection
Multi-objective optimization
Artificial immune algorithm
Dynamic optimization
Constrained optimization
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Snippet Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal...
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SubjectTerms Artificial immune algorithm
Clonal selection
Constrained optimization
Dynamic optimization
Multi-objective optimization
Title A survey of artificial immune algorithms for multi-objective optimization
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