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Computational Intelligence and Neuroscience Volume 2018 ,2018-11-01
An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework
Research Article
Jiuyuan Huo 1 , 2 Liqun Liu 3
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DOI:10.1155/2018/5865168
Received 2018-06-11, accepted for publication 2018-09-27, Published 2018-09-27
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摘要

The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been integrated into the common multiobjective optimization frameworks which provide the integrated environments for understanding, reusing, implementation, and comparison of multiobjective algorithms. Therefore, a unified, flexible, configurable, and user-friendly MOABC algorithm framework is presented which combines a multiobjective ABC algorithm named RMOABC and the multiobjective evolution algorithms (MOEA) framework in this paper. The multiobjective optimization framework aims at the development, experimentation, and study of metaheuristics for solving multiobjective optimization problems. The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application. The experiment’s results showed the framework can deal with practical multiobjective optimization problems more effectively and flexibly, can provide comprehensive and reliable parameters sets, and can complete reference, comparison, and analysis tasks among multiple optimization algorithms.

授权许可

Copyright © 2018 Jiuyuan Huo and Liqun Liu. 2018
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.

图表

General architecture of the MOEA framework.

UML diagram of the MOABC algorithm and its variants.

UOF-MOABC general optimization framework.

Plots of the final nondominated solution set of the seven algorithms on the 3 objectives of WFG9. (a) NSGA-II algorithm. (b) NSGA-III algorithm. (c) ε-MOEA algorithm. (d) SMPSO algorithm. (e) MOEA/D algorithm. (f) GDE3 algorithm. (g) RMOABC algorithm.

Plots of the final nondominated solution set of the seven algorithms on the 3 objectives of WFG9. (a) NSGA-II algorithm. (b) NSGA-III algorithm. (c) ε-MOEA algorithm. (d) SMPSO algorithm. (e) MOEA/D algorithm. (f) GDE3 algorithm. (g) RMOABC algorithm.

Plots of the final nondominated solution set of the seven algorithms on the 3 objectives of WFG9. (a) NSGA-II algorithm. (b) NSGA-III algorithm. (c) ε-MOEA algorithm. (d) SMPSO algorithm. (e) MOEA/D algorithm. (f) GDE3 algorithm. (g) RMOABC algorithm.

Plots of the final nondominated solution set of the seven algorithms on the 3 objectives of WFG9. (a) NSGA-II algorithm. (b) NSGA-III algorithm. (c) ε-MOEA algorithm. (d) SMPSO algorithm. (e) MOEA/D algorithm. (f) GDE3 algorithm. (g) RMOABC algorithm.

Plots of the final nondominated solution set of the seven algorithms on the 3 objectives of WFG9. (a) NSGA-II algorithm. (b) NSGA-III algorithm. (c) ε-MOEA algorithm. (d) SMPSO algorithm. (e) MOEA/D algorithm. (f) GDE3 algorithm. (g) RMOABC algorithm.

Plots of the final nondominated solution set of the seven algorithms on the 3 objectives of WFG9. (a) NSGA-II algorithm. (b) NSGA-III algorithm. (c) ε-MOEA algorithm. (d) SMPSO algorithm. (e) MOEA/D algorithm. (f) GDE3 algorithm. (g) RMOABC algorithm.

Plots of the final nondominated solution set of the seven algorithms on the 3 objectives of WFG9. (a) NSGA-II algorithm. (b) NSGA-III algorithm. (c) ε-MOEA algorithm. (d) SMPSO algorithm. (e) MOEA/D algorithm. (f) GDE3 algorithm. (g) RMOABC algorithm.

The performance indicators of Δp, SP, and HV vs. the iteration number of the seven multiobjective algorithms for the WFG9 problem with 3 objectives. (a)Δp indicator. (b) Spacing (SP) indicator. (c) Hypervolume (HV) indicator.

The performance indicators of Δp, SP, and HV vs. the iteration number of the seven multiobjective algorithms for the WFG9 problem with 3 objectives. (a)Δp indicator. (b) Spacing (SP) indicator. (c) Hypervolume (HV) indicator.

The performance indicators of Δp, SP, and HV vs. the iteration number of the seven multiobjective algorithms for the WFG9 problem with 3 objectives. (a)Δp indicator. (b) Spacing (SP) indicator. (c) Hypervolume (HV) indicator.

Normalized ranges of the five objective functions’ values of the water problem obtained by the seven algorithms.

The performance indicator of HV vs. the iteration number of the seven multiobjective algorithms for WRP problem.

通讯作者

Jiuyuan Huo.School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China, lzjtu.edu.cn;Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China, cas.cn.huojy@foxmail.com

推荐引用方式

Jiuyuan Huo,Liqun Liu. An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework. Computational Intelligence and Neuroscience ,Vol.2018(2018)

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