Abstract
In view of the unsatisfactory search performance of binary crossing operator as well as the elitist-preserving approach’s influence on the population’s diversity, an algorithm of multi-objective based on layer strategy and self-adaptive crossing distribution index is put forward on the basis of research and analysis on NSGA-II algorithm. The algorithm will be applied to the ZDT series test functions. The experiment results show that the improved algorithm maintains the diversity and distribution of population. Compared with NSGA-II, the Pareto front we get is much closer to the true Pareto optimal front.
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Zhao, S., Hao, Z., Liu, S., Xu, W., Huang, H. (2012). Multi-objective Evolutionary Algorithm Based on Layer Strategy. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_66
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DOI: https://doi.org/10.1007/978-3-642-30976-2_66
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