Elite-guided multi-objective artificial bee colony algorithm

doi:10.1016/j.asoc.2015.03.040

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Elite-guided multi-objective artificial bee colony algorithm

2015-07-01

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DOI：10.1016/j.asoc.2015.03.040

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Abstract

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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. (C) 2015 Elsevier B.V. All rights reserved.

Keywords:

Multi-objective optimization

Evolutionary algorithm

Artificial bee colony

Multi-objective artificial bee colony

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