A NEW MULTI-OBJECTIVE ARTIFICIAL BEE COLONY ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS
Züleyha Yılmaz Acar, Fikri Aydemir, Fatih Başçi̇ftçi̇ · 2018
Since real-world problems have multi-objective optimization problems, algorithms that solve such problems are getting more important. In this study, a new multi-objective artificial bee colony algorithm is proposed for solving multi-objective optimization problems. With the proposed algorithm, non-dominated solutions are kept in the fixed-sized archive. It has benefited from the crowding distance during the selection of elite solutions in the archive. Moreover, the onlooker bees are selected from the archive members with the proposed algorithm. It is aimed to improve the archive members with this modification. To evaluate the performance of the proposed algorithm, ZDT1, ZDT2 and ZDT3 from ZDT family of benchmark functions were used as multi-objective benchmark problems and the results were compared with MOPSO and NSGA-II algorithms. The results show that the proposed algorithm is an alternative method for multi-objective optimization problems.