A competitive and bi-space sparsity-based artificial bee colony algorithm for large-scale multi-objective optimisation
Jiayao Qian, Jinyu Xu, Shitao Liao, Hui Wang · International Journal of Bio-Inspired Computation · 2025
This paper proposes a novel artificial bee colony (ABC) algorithm, called LMOABC, to enhance the global search capability of ABC in solving large-scale multi-objective optimisation problems (LSMOPs). By introducing a competitive mechanism, the population is segregated into two distinct groups: 'losers' and 'winners'. To achieve a trade-off between exploration and exploitation, employed bees perform the global search on the 'losers', while onlooker bees conduct the local search on the 'winners'. Moreover, a bi-space sparsity method is designed in the scout bee stage, in which stagnated solutions are re-initialised to sparse regions. To verify the performance of LMOABC, nine well-known LSMOP benchmark problems with dimensions of 100, 500 and 1,000 are tested. Experimental results show that it performs competitively compared to several recent LSMOEAs.