Elite-guided multi-objective artificial bee colony algorithm

•A novel multi-objective optimization algorithm based on ABC is proposed.•The elite-guided solution generation strategy is proposed to exploit the neighborhood of the solutions based on the guidance of the elite.•A novel fitness calculation method is presented to calculate the selecting probability.•The proposed approach is highly competitive with other algorithms.

Multi-objective optimization has been a difficult problem and a research focus in the field of science and engineering. This paper presents a novel multi-objective optimization algorithm called elite-guided multi-objective artificial bee colony (EMOABC) algorithm. In our proposal, the fast non-dominated sorting and population selection strategy are applied to measure the quality of the solution and select the better ones. The elite-guided solution generation strategy is designed to exploit the neighborhood of the existing solutions based on the guidance of the elite. Furthermore, a novel fitness calculation method is presented to calculate the selecting probability for onlookers. The proposed algorithm is validated on benchmark functions in terms of four indicators: GD, ER, SPR, and TI. The experimental results show that the proposed approach can find solutions with competitive convergence and diversity within a shorter period of time, compared with the traditional multi-objective algorithms. Consequently, it can be considered as a viable alternative to solve the multi-objective optimization problems.

Graphical abstractThe figure shows the PF produced by each algorithm on the two-objective test functions ZDT. It can be observed that our proposed algorithm EMOABC can produce competitive results that not only have good convergence but also have appropriate distribution over other algorithms.Figure optionsDownload full-size imageDownload as PowerPoint slide