Abstract
This work presents a new multiobjective optimization algorithm based on artificial bee colony, named the ICABCMOA. In order to meet the requirements of Pareto-based approaches, a new fitness assignment function is defined based on the dominated number. In the ICABCMOA, a high-dimension chaotic method based on Tent map is addressed to increase the searching efficiency. Vaccination and gene recombination are adopted to promote the convergence. The experimental results of the ICABCMOA compared with NSGAII and SPEA2 over a set of test functions show that it is an effective method for high-dimension optimization problems. © 2013 Springer-Verlag Berlin Heidelberg.
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Zhou, X., Shen, J., & Li, Y. (2013). Immune based chaotic artificial bee colony multiobjective optimization algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7928 LNCS, pp. 387–395). https://doi.org/10.1007/978-3-642-38703-6_46
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