About: Biogeography-based optimization (BBO) algorithm is not good at dealing with regions where function values change dramatically or barely. A novel biogeography-based optimization algorithm is proposed in this paper based on Momentum migration and taxonomic mutation. The momentum item is added to the original migration operation of BBO. It makes the algorithm more advantageous in dealing with regions where function values change dramatically or barely. At the same time, taxonomic mutation strategy divides the solutions into three categories: promising class, middle class and inferior class. Promising solutions do not take part in this mutation operation. Solutions of middle class use balanced differential mutation, and inferior solutions adopt exploration-biased random mutation. This strategy further increases the diversity of population. The simulation experiments are carried out with different types of CEC2014 benchmark functions. The proposed algorithm is compared with other algorithms and shows stronger global search ability, faster convergence speed and higher convergence accuracy.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : wasabi.inria.fr associated with source document(s)

AttributesValues
type
value
  • Biogeography-based optimization (BBO) algorithm is not good at dealing with regions where function values change dramatically or barely. A novel biogeography-based optimization algorithm is proposed in this paper based on Momentum migration and taxonomic mutation. The momentum item is added to the original migration operation of BBO. It makes the algorithm more advantageous in dealing with regions where function values change dramatically or barely. At the same time, taxonomic mutation strategy divides the solutions into three categories: promising class, middle class and inferior class. Promising solutions do not take part in this mutation operation. Solutions of middle class use balanced differential mutation, and inferior solutions adopt exploration-biased random mutation. This strategy further increases the diversity of population. The simulation experiments are carried out with different types of CEC2014 benchmark functions. The proposed algorithm is compared with other algorithms and shows stronger global search ability, faster convergence speed and higher convergence accuracy.
subject
  • Mutation
  • Algorithms
  • Molecular evolution
  • Evolutionary biology
  • Physical geography
  • Radiation health effects
  • Social classes
  • Mathematical logic
  • Theoretical computer science
  • Nature-inspired metaheuristics
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software