File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.5220/0006014201050114
- Scopus: eid_2-s2.0-85006409624
- WOS: WOS:000393153900010
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: A Hybrid Multi-objective Immune Algorithm for Numerical Optimization
Title | A Hybrid Multi-objective Immune Algorithm for Numerical Optimization |
---|---|
Authors | |
Keywords | Artificial Immune Systems Artificial Intelligence Multi-objective Optimization Genetic Algorithm Hybrid Algorithm |
Issue Date | 2016 |
Publisher | SCITEPRESS – Science and Technology Publications. |
Citation | Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016), Porto, Portugal, 9-11 November 2016, v. 1: ECTA, p. 105-114 How to Cite? |
Abstract | With the complexity of real world problems, optimization of these problems often has multiple objectives to be considered simultaneously. Solving this kind of problems is very difficult because there is no unique
solution, but rather a set of trade-off solutions. Moreover, evaluating all possible solutions requires
tremendous computer resources that normally are not available. Therefore, an efficient optimization
algorithm is developed in this paper to guide the search process to the promising areas of the solution space for obtaining the optimal solutions in reasonable time, which can aid the decision makers in arriving at an optimal solution/decision efficiently. In this paper, a hybrid multi-objective immune optimization algorithm based on the concepts of the biological evolution and the biological immune system including clonal selection and expansion, affinity maturation, metadynamics, immune suppression and crossover is developed. Numerical experiments are conducted to assess the performance of the proposed hybrid algorithm using several benchmark problems. Its performance is measured and compared with other wellknown multi-objective optimization algorithms. The results show that for most cases the proposed hybrid algorithm outperforms the other benchmarking algorithms especially in terms of solution diversity. |
Description | 8th ECTA (8th International Conference on Evolutionary Computation Theory and Applications ) is part of IJCCI, the 8th International Joint Conference on Computational Intelligence. |
Persistent Identifier | http://hdl.handle.net/10722/262550 |
ISBN | |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Leung, CSK | - |
dc.contributor.author | Lau, HYK | - |
dc.date.accessioned | 2018-09-28T05:01:15Z | - |
dc.date.available | 2018-09-28T05:01:15Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016), Porto, Portugal, 9-11 November 2016, v. 1: ECTA, p. 105-114 | - |
dc.identifier.isbn | 978-989-758-201-1 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262550 | - |
dc.description | 8th ECTA (8th International Conference on Evolutionary Computation Theory and Applications ) is part of IJCCI, the 8th International Joint Conference on Computational Intelligence. | - |
dc.description.abstract | With the complexity of real world problems, optimization of these problems often has multiple objectives to be considered simultaneously. Solving this kind of problems is very difficult because there is no unique solution, but rather a set of trade-off solutions. Moreover, evaluating all possible solutions requires tremendous computer resources that normally are not available. Therefore, an efficient optimization algorithm is developed in this paper to guide the search process to the promising areas of the solution space for obtaining the optimal solutions in reasonable time, which can aid the decision makers in arriving at an optimal solution/decision efficiently. In this paper, a hybrid multi-objective immune optimization algorithm based on the concepts of the biological evolution and the biological immune system including clonal selection and expansion, affinity maturation, metadynamics, immune suppression and crossover is developed. Numerical experiments are conducted to assess the performance of the proposed hybrid algorithm using several benchmark problems. Its performance is measured and compared with other wellknown multi-objective optimization algorithms. The results show that for most cases the proposed hybrid algorithm outperforms the other benchmarking algorithms especially in terms of solution diversity. | - |
dc.language | eng | - |
dc.publisher | SCITEPRESS – Science and Technology Publications. | - |
dc.relation.ispartof | IJCCI (International Joint Conference on Computational Intelligence) 2016 - ECTA | - |
dc.subject | Artificial Immune Systems | - |
dc.subject | Artificial Intelligence | - |
dc.subject | Multi-objective Optimization | - |
dc.subject | Genetic Algorithm | - |
dc.subject | Hybrid Algorithm | - |
dc.title | A Hybrid Multi-objective Immune Algorithm for Numerical Optimization | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lau, HYK: hyklau@hku.hk | - |
dc.identifier.authority | Lau, HYK=rp00137 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.5220/0006014201050114 | - |
dc.identifier.scopus | eid_2-s2.0-85006409624 | - |
dc.identifier.hkuros | 293303 | - |
dc.identifier.volume | 1: ECTA | - |
dc.identifier.spage | 105 | - |
dc.identifier.epage | 114 | - |
dc.identifier.isi | WOS:000393153900010 | - |