An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection
| dc.cclicence | CC-BY-NC-ND | en |
| dc.contributor.author | Qiao, Junfei | |
| dc.contributor.author | Li, Fei | |
| dc.contributor.author | Yang, Shengxiang | |
| dc.contributor.author | Yang, Cuili | |
| dc.contributor.author | Li, Wenjing | |
| dc.contributor.author | Gu, Ke | |
| dc.date.acceptance | 2019-08-11 | |
| dc.date.accessioned | 2019-08-29T13:54:22Z | |
| dc.date.available | 2019-08-29T13:54:22Z | |
| dc.date.issued | 2019-08-12 | |
| dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | en |
| dc.description.abstract | In general, for the iteration process of an evolutionary algorithm (EA), there exists the problem of uneven distribution of individuals in the target space for both multi-objective and single-objective optimization problems. This uneven distribution significantly degrades the population diversity and convergence speed. This paper proposes an adaptive hybrid evolutionary immune algorithm based on a uniform distribution selection mechanism (AUDHEIA) for solving MOPs efficiently. In AUDHEIA, the individuals in the population are mapped to a hyperplane, which is correlated with the objective space and are clustered to increase the diversity of solutions. To improve the distribution of the solutions, the mapped hyperplane is evenly sectioned. With the constantly changing distribution during the iteration, a threshold as a standard for judging the distribution level is adjusted adaptively. When the threshold is not satisfied in the corresponding interval, the distribution enhancement module is activated. Then, the same number of individuals should be selected in each interval. However, sometimes, there are insufficient or no individuals in the interval during the iterative process. To obtain sufficient individuals, the limit optimization variation strategy of the best individual is adopted. Experiments show that this algorithm can escape from local optima and has a high convergence speed. Moreover, the distribution and convergence of this algorithm are superior to the peer algorithms tested in this paper. | en |
| dc.funder | Other external funder (please detail below) | en |
| dc.funder.other | National Natural Science Foundation of China | en |
| dc.identifier.citation | Qiao, J., Li, F., Yang, S., Yang,C., Li, W. and Gu, K. (2019) An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection. Information Sciences, | en |
| dc.identifier.doi | https://doi.org/10.1016/j.ins.2019.08.032 | |
| dc.identifier.uri | https://www.dora.dmu.ac.uk/handle/2086/18373 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | 61603012 | en |
| dc.projectid | 61533002 | en |
| dc.projectid | 61603009 | en |
| dc.publisher | Elsevier | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Immune algorithm | en |
| dc.subject | Uniform distribution selection | en |
| dc.subject | Distribution enhancement | en |
| dc.subject | Local variation | en |
| dc.subject | Multi-objective optimization | en |
| dc.title | An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection | en |
| dc.type | Article | en |