Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems
In order to improve the performance of optimization, we apply a hybridization of adaptive biogeography-based optimization (BBO) algorithm and differential evolution (DE) to multi-objective optimization problems (MOPs). A model of multi-objective evolutionary algorithms (MOEAs) is established, in whi...
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doaj-cb8e753a9b4e48c49b255c5874b33d362020-11-25T00:16:49ZengMDPI AGInformation2078-24892017-07-01838310.3390/info8030083info8030083Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization ProblemsSiling Feng0Ziqiang Yang1Mengxing Huang2College of Information Science&Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, ChinaCollege of Information Science&Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, ChinaCollege of Information Science&Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, ChinaIn order to improve the performance of optimization, we apply a hybridization of adaptive biogeography-based optimization (BBO) algorithm and differential evolution (DE) to multi-objective optimization problems (MOPs). A model of multi-objective evolutionary algorithms (MOEAs) is established, in which the habitat suitability index (HSI) is redefined, based on the Pareto dominance relation, and density information among the habitat individuals. Then, we design a new algorithm, in which the modification probability and mutation probability are changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. The mutation operators based on DE algorithm, are modified, and the migration operators based on number of iterations, are improved to achieve better convergence performance. Numerical experiments on different ZDT and DTLZ benchmark functions are performed, and the results demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution properties comparing to the other MOEAs, and can solve more complex multi-objective optimization problems efficiently.https://www.mdpi.com/2078-2489/8/3/83multi-objective optimizationMABBOadaptive biogeography-based optimizationdifferential evolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Siling Feng Ziqiang Yang Mengxing Huang |
spellingShingle |
Siling Feng Ziqiang Yang Mengxing Huang Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems Information multi-objective optimization MABBO adaptive biogeography-based optimization differential evolution |
author_facet |
Siling Feng Ziqiang Yang Mengxing Huang |
author_sort |
Siling Feng |
title |
Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems |
title_short |
Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems |
title_full |
Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems |
title_fullStr |
Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems |
title_full_unstemmed |
Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems |
title_sort |
hybridizing adaptive biogeography-based optimization with differential evolution for multi-objective optimization problems |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2017-07-01 |
description |
In order to improve the performance of optimization, we apply a hybridization of adaptive biogeography-based optimization (BBO) algorithm and differential evolution (DE) to multi-objective optimization problems (MOPs). A model of multi-objective evolutionary algorithms (MOEAs) is established, in which the habitat suitability index (HSI) is redefined, based on the Pareto dominance relation, and density information among the habitat individuals. Then, we design a new algorithm, in which the modification probability and mutation probability are changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. The mutation operators based on DE algorithm, are modified, and the migration operators based on number of iterations, are improved to achieve better convergence performance. Numerical experiments on different ZDT and DTLZ benchmark functions are performed, and the results demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution properties comparing to the other MOEAs, and can solve more complex multi-objective optimization problems efficiently. |
topic |
multi-objective optimization MABBO adaptive biogeography-based optimization differential evolution |
url |
https://www.mdpi.com/2078-2489/8/3/83 |
work_keys_str_mv |
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1725382354324684800 |