Enhancing the performance of biogeography-based optimization using polyphyletic migration operator and orthogonal learning

2014 ◽  
Vol 41 ◽  
pp. 125-139 ◽  
Author(s):  
Guojiang Xiong ◽  
Dongyuan Shi ◽  
Xianzhong Duan
Keyword(s):  
Migration Operator ◽  
Orthogonal Learning

scholarly journals Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Siao Wen ◽  
Jinfu Chen ◽  
Yinhong Li ◽  
Dongyuan Shi ◽  
Xianzhong Duan
Keyword(s):  
Orthogonal Design ◽  
The Other ◽  
Feasible Region ◽  
Migration Process ◽  
Migration Strategy ◽  
Solution Quality ◽  
Migration Operator ◽  
Global Topology ◽  
Convergence Performance ◽  
Orthogonal Learning

Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms. The effects of each improved component are also analyzed.

NBBO: A new variant of biogeography-based optimization with a novel framework and a two-phase migration operator

2019 ◽  
Vol 504 ◽  
pp. 178-201 ◽  
Author(s):  
Ali Reihanian ◽  
Mohammad-Reza Feizi-Derakhshi ◽  
Hadi S. Aghdasi
Keyword(s):  
Two Phase ◽  
Migration Operator ◽  
New Variant
Sign in / Sign up

Export Citation Format

Share Document