Abstract

Artificial bee colony algorithm (ABC) and particle swarm optimization (PSO) are both famous optimization algorithms that have been successfully applied to various optimization problems, especially in function optimization. Those two algorithms have been attracting more and more research interest because of their efficiency and simplicity. However, PSO has poor exploration capabilities and thus is easy to fall into the local optimum; Likewise, ABC has low convergence speed. To address these shortcomings, firstly, we improved the ABC with the combination of greedy selection and crossover, secondly, a sine-cosine method will be used to help PSO jump into local optimal. Finally, a new hybrid algorithm based on improved ABC and PSO are proposed. Moreover, four functions are used to verify the effectiveness of the proposed algorithm, and the results show that, compared with other well-known algorithms, ABC-PSO is more efficient, faster and more robust in function optimization.

Details

Title
A New Hybrid Algorithm Based on ABC and PSO for Function Optimization
Author
Chang-Feng, Chen 1 ; Azlan Mohd Zain 2 ; Li-Ping, Mo 1 ; Kai-Qing Zhou 1 

 College of Information Science and Engineering, Jishou University, Jishou, Hunan, China 
 UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, University Teknologi Malaysia, Johor, Malaysia 
Publication year
2020
Publication date
May 2020
Publisher
IOP Publishing
ISSN
17578981
e-ISSN
1757899X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2562530294
Copyright
© 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.