Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization
Open accessAbstract
Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population‐based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well‐known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.
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Zhu et al. propose a hybrid training algorithm based on global optimization, the PSO-ABC technique, an artificial bee colony algorithm employing particle swarm optimization.
“In contrast to the local optimization in BP, Zhu et al. [500] propose a hybrid training algorithm that is based on global optimization, the PSO-ABC technique [98].It is an artificial bee colony (ABC) algorithm employing particle swarm optimization (PSO), an evolutionary search algorithm.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [98]
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“This algorithm is utilized for solving numerical and nonnumerical optimization problems from its producing [8].”
Method
- Authors
- Chun-Feng, W., Kui, L., & Pei-Ping, S.
- Venue
- Mathematical Problems in Engineering
- Publisher
- Wiley
- Published
- 2014-01
- Volume
- 2014
- Issue
- 1
- ISSN
- 1024-123X (ISSN-L)
- 1563-5147
- OpenAlex
- W1986552887
- Semantic Scholar
- 4e8f41bb9f5f57315b4d152af545fa46aa31609e
- Cited by
- 38
- References
- 20
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2022: 4 · 2021: 9 · 2020: 3 · 2019: 5 · 2018: 6 · 2017: 2 · 2016: 3 · 2015: 1
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Mathematical Problems in Engineering (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (U1404105, 11171094, 142102210058, qd12103, 2013qk02, 01016400105)
- Key Scientific and Technological Project of Henan Province (U1404105, 11171094, 142102210058, qd12103, 2013qk02, 01016400105)
- Doctoral Scientific Research Foundation of Henan Normal University (U1404105, 11171094, 142102210058, qd12103, 2013qk02, 01016400105)
- Youth Science Foundation of Henan Normal University (U1404105, 11171094, 142102210058, qd12103, 2013qk02, 01016400105)
- Henan Normal University National Research Project (U1404105, 11171094, 142102210058, qd12103, 2013qk02, 01016400105)
- Henan Normal University Youth Backbone Teacher Training (U1404105, 11171094, 142102210058, qd12103, 2013qk02, 01016400105)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Life Sciences › Agricultural and Biological Sciences › Insect Science › Bee Products Chemical Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- artificial bee colony algorithm, particle swarm optimization, hybrid metaheuristic, global optimization, exploration-exploitation balance, swarm intelligence, convergence rate
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