Multi-Objective Artificial Bee Colony Algorithm
Yanjiao Wang, Yaojie Li · 2015
In order to approach the true Pareto front as fast as possible and make the distribution of solutions uniform on multi-objective optimization problems, a multi-objective optimization algorithm based on artificial bee colony algorithm has been presented in this paper, named MABC. Firstly, a novel selection scheme, which is used to guide the population evolution towards the true Pareto front and keep population diversified, substitutes the roulette wheel selection scheme. Secondly, the adaptive searching models are designed for the employed bees and onlookers, in which the convergence rate and diversity are considered simultaneously. Finally, an improved method of determining elite population is proposed to maintain diversity. Compared with other state-of-the-art algorithms, the simulation results of 5 standard test functions show that MABC achieves comparable results in terms of diversity and convergence metrics.