Numer. Math. Theor. Meth. Appl., 12 (2019), pp. 797-823.
Published online: 2019-04
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Artificial bee colony (ABC) algorithm is one of the most popular swarm intelligence algorithms. Owing to its simpleness and effectiveness, it has been widely applied in many fields. Many modified versions of ABC algorithm were used to solve constrained optimization problems (COPs). This paper introduces an artificial bee colony algorithm based on multiobjective and nondominated solution replacement mechanism (MONABC) for solving COPs. This new approach presents four modifications on the foundation of the original ABC algorithm. The COPs are converted into unconstrained multiobjective optimization problems (MOPs), and the hybrid search mechanism of small population is applied in the employed bee phase. Moreover, nondominated solution replacement mechanism is devoted to updating the population. According to the dominating ability and feasibility, a new following probability formula based on the overall rank is proposed. In the scout bee phase, new archive and replacement mechanism will be constructed. To verify the performance of our approach, MONABC algorithm is tested on 24 and 18 well-known constrained problems from 2006 and 2010 IEEE Congress on Evolution Computation (CEC 2006 and 2010). The results indicate that MONABC is competitive with the state-of-the-art algorithms for solving COPs and MOPs.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2017-0098}, url = {http://global-sci.org/intro/article_detail/nmtma/13131.html} }Artificial bee colony (ABC) algorithm is one of the most popular swarm intelligence algorithms. Owing to its simpleness and effectiveness, it has been widely applied in many fields. Many modified versions of ABC algorithm were used to solve constrained optimization problems (COPs). This paper introduces an artificial bee colony algorithm based on multiobjective and nondominated solution replacement mechanism (MONABC) for solving COPs. This new approach presents four modifications on the foundation of the original ABC algorithm. The COPs are converted into unconstrained multiobjective optimization problems (MOPs), and the hybrid search mechanism of small population is applied in the employed bee phase. Moreover, nondominated solution replacement mechanism is devoted to updating the population. According to the dominating ability and feasibility, a new following probability formula based on the overall rank is proposed. In the scout bee phase, new archive and replacement mechanism will be constructed. To verify the performance of our approach, MONABC algorithm is tested on 24 and 18 well-known constrained problems from 2006 and 2010 IEEE Congress on Evolution Computation (CEC 2006 and 2010). The results indicate that MONABC is competitive with the state-of-the-art algorithms for solving COPs and MOPs.