Abstract: To deal with the multi-objective optimization problem， the multi-objective JAYA algorithm based on decomposition is proposed. Based on the decomposition-based multi-objective algorithm， the JAYA algorithm is extended to the multi-objective optimization field. Meanwhile， a Lévy flight strategy is introduced to enhance the perturbation of the algorithm， and a feedback learning phase is added to improve the individual learning ability， resulting in the improvement of the algorithm’s diversity and the ability of global optimization search. To verify the performance of the proposed algorithm， it is compared with several classical multi-objective algorithms on the ZDT and DTLZ test functions. The results show that MOJAYA/D outperforms the other algorithms in both convergence and diversity. Finally， the proposed algorithm is applied to the image segmentation problem under multiple objective criteria. The segmentation results show that MOJAYA/D is very effective in dealing with image segmentation problems.

Key words: JAYA algorithm, multi-objective optimization, feedback learning, image segmentation