A global best artificial bee colony algorithm for global optimization

By Weifeng Gao, Sanyang Liu, Lingling Huang. Journal of Computational and Applied Mathematics, 2012

446 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Modeling and Simulation, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/908a74eb2272a0d7bae9fb200fa6b472c8a95515

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sedigheh Mahdavi
2. Kalyanmoy Deb
3. Da-Gang Fang
4. A-Min Yao
5. Wen Wu
6. Shahryar Rahnamayan
7. Jiongming Jiang
8. Tinghuai Ma

In Modeling and Simulation

1. Da-Gang Fang
2. Kalyanmoy Deb
3. A-Min Yao
4. Sedigheh Mahdavi
5. Wen Wu
6. Shahryar Rahnamayan

In Numerical Analysis

1. Da-Gang Fang
2. Kalyanmoy Deb
3. A-Min Yao
4. Sedigheh Mahdavi
5. Wen Wu
6. Shahryar Rahnamayan

Citations in the corpus

• Opposition based learning: A literature review (2017, methodology): “An initial population was generated for the ABC algorithm by using OBL and chaotic systems in [86,84,85,65,363].”
• Frequency Diverse Array Antenna Using Time-Modulated Optimized Frequency Offset to Obtain Time-Invariant Spatial Fine Focusing Beampattern (2016, methodology): “The ABC algorithm is preferred because it is simple, flexible, and robust in comparison with other swarm based algorithms [37]–[39].”
• A self-adaptive artificial bee colony algorithm based on global best for global optimization (2017, methodology): “The performance of SABC-GB was compared with that of GABC (Zhu and Kwong 2010), ABC/best/1 (Gao et al. 2012), and ABC/best/2 (Gao et al. 2012).”