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Information 2017, 8(1), 18; doi:10.3390/info8010018

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

1
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2
Gansu Data Engineering and Technology Research Center for Resources and Environment, Lanzhou 730000, China
3
College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Academic Editor: Günter Neumann
Received: 29 November 2016 / Revised: 24 January 2017 / Accepted: 31 January 2017 / Published: 3 February 2017
(This article belongs to the Section Artificial Intelligence)
View Full-Text   |   Download PDF [2254 KB, uploaded 3 February 2017]   |  

Abstract

To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time. View Full-Text
Keywords: multi-objective optimization; Artificial Bee Colony algorithm; regulation operator; adaptive grid multi-objective optimization; Artificial Bee Colony algorithm; regulation operator; adaptive grid
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huo, J.; Liu, L. An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators. Information 2017, 8, 18.

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