Abstract
Based on clonal selection principle, an improved immune algorithm (IIA) is proposed in this paper. This algorithm generates the next population under the guidance of the previous superior antibodies (Ab’s) in a small and a large neighborhood respectively, in order to realize the parallel global and local search capabilities. The computational results show that higher quality solutions are obtained in a shorter time, and the degree of diversity in population are maintained by the proposed method. Meanwhile, “Average truncated generations” and “Distribution entropy of truncated generations” are used to evaluate the optimization efficiency of IIA. The comparison with clonal selection algorithm (CSA) demonstrates the superiority of the proposed algorithm IIA.
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Keywords
- Particle Swarm Optimization
- Artificial Immune System
- Benchmark Function
- Optimization Efficiency
- Clonal Selection Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhu, C., Zhao, B., Ye, B., Cao, Y. (2005). An Improved Immune Algorithm and Its Evaluation of Optimization Efficiency. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_126
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DOI: https://doi.org/10.1007/11539117_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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