A new multi-objective artificial bee colony algorithm based on reference point and opposition

A new multi-objective artificial bee colony (ABC) algorithm based on reference point and opposition (called ROMOABC) is proposed in this paper. Firstly, the original framework of ABC is modified to improve the efficiency of population renewal and accelerate the convergence rate. On the basis of this framework, two new strategies are proposed. In the scout bee search, opposition-based learning and elite solutions are used to reduce the waste of computing resources. Distribution of solutions is improved by using reference points' associated external archive. Experiments are conducted on 16 multi-objective benchmark functions including ZDT, DTLZ and WFG multi-objective benchmark functions. The comparison of ROMOABC with five other multi-objective algorithms shows that it has competitive convergence and diversity.

Automated summary

This paper proposes a new multi-objective artificial bee colony algorithm called ROMOABC, which is based on reference point and opposition. Experimental results on multiple benchmark functions demonstrate that ROMOABC achieves competitive convergence and diversity.