.png){kind=icon}
Return
A hybrid immune multiobjective optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2009.10.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Multiple objective programming
Artificial immune systems
Clonal selection principle
Hybrid mutation
Artificial intelligence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
Cited Papers
Cytokine-induced inside-out activation of FcαR (CD89) is mediated by a single serine residue (S263) in the intracellular domain of the receptor.png)
Blood
OAAI
BloodIF0
OAAIShareSaveShareSaveShareSaveShareSave
View more