A Novel Hybrid Optimization Algorithm Combined with BBO and ACO

This paper proposes a novel hybrid optimization algorithm based biogeography-based optimization (BBO) and ant colony optimization (ACO). Specifically, BBO is utilized for preliminary search and then the final search is conducted by using ACO on the preliminary results, which makes the hybrid algorit...

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Bibliographic Details
Published inChinese Control Conference pp. 1581 - 1586
Main Authors Dai, Zhuo, Ma, Qian, Zhao, Dongdong, Yan, Shi
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2020
Subjects
Ant colony optimization
Biogeography-Based Optimization
Biological system modeling
Hybrid Optimization Algorithm
Optimization
Sociology
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ISSN1934-1768
DOI10.23919/CCC50068.2020.9189315

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Summary:This paper proposes a novel hybrid optimization algorithm based biogeography-based optimization (BBO) and ant colony optimization (ACO). Specifically, BBO is utilized for preliminary search and then the final search is conducted by using ACO on the preliminary results, which makes the hybrid algorithm have powerful search ability in the solution space. Moreover, the algorithm is tested on 6 well-known benchmarks for comparing with the 4 others existing optimization algorithms, including 2 algorithms were proposed by the coauthor earlier. Obviously, the simulation results show that the proposed hybrid BBO-ACO has better performance than the existing others.
ISSN:1934-1768
DOI:10.23919/CCC50068.2020.9189315