Abstract
In this paper, we introduce a new multiobjective optimization (MO) algorithm to solve ZDT test problems using the immune clonal principle. This algorithm is termed Immune Clonal MO Algorithm (ICMOA). In ICMOA, the antibody population is split into nondominated antibodies and dominated antibodies. Meanwhile, the nondominated antibodies are allowed to survive and to clone and the nonuniform mutation is adopted. Two metrics proposed by K. Deb et al. are adopted to measure the extent of convergence to a known set of Pareto-optimal solutions and the extent of spread achieved among the obtained solutions. Our algorithm is compared with another algorithm that is representative of the state-of-the-art in evolutionary multiobjective optimization–NSGA-II. Simulation results on ZDT test problems show that ICMOA, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to NSGA-II.
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Shang, R., Ma, W. (2006). Immune Clonal MO Algorithm for ZDT Problems. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_13
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DOI: https://doi.org/10.1007/11881223_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
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