A Multiobjective Artificial Bee Colony Algorithm based on Decomposition

Guang Peng, Zhihao Shang, Katinka Wolter


This paper presents a multiobjective artificial bee colony (ABC) algorithm using the decomposition approach for improving the performance of MOEA/D (multiobjective evolutionary algorithm based on decomposition). Using a novel reproduction operator inspired by ABC, we propose MOEA/D-ABC, a new version of MOEA/D. Then, a modified Tchebycheff approach is adopted to achieve higher diversity of the solutions. Further, an adaptive normalization operator can be incorporated into MOEA/D-ABC to solve the differently scaled problems. The proposed MOEA/D-ABC is compared to several state-of-the-art algorithms on two well-known test suites. The experimental results show that MOEA/D-ABC exhibits better convergence and diversity than other MOEA/D algorithms on most instances.


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