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A Comparison of Improved Artificial Bee Colony Algorithms Based on Differential Evolution
| Content Provider | Semantic Scholar |
|---|---|
| Author | Qiu, Jianfeng Wang, Jiwen Yang, Dan |
| Copyright Year | 2013 |
| Abstract | The Artificial Bee Colony (ABC) algorithm is an active field of optimization based on swarm intelligence in recent years. Inspired by the mutation strategies used in Differential Evolution (DE) algorithm, this paper introduced three types strategies (“rand”, “best”, and “current-to-best”) and one or two numbers of disturbance vectors to ABC algorithm. Although individual mutation strategies in DE have been used in ABC algorithm by some researchers in different occasions, there have not a comprehensive application and comparison of the mutation strategies used in ABC algorithm. In this paper, these improved ABC algorithms can be analyzed by a set of testing functions including the rapidity of the convergence. The results show that those improvements based on DE achieve better performance in the whole than basic ABC algorithm. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iaescore.com/journals/index.php/IJEECS/article/download/2680/3706 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm Artificial bee colony algorithm Bees Biological Evolution Convergence (action) Differential evolution Genetic algorithm Mathematical optimization Mutation Rand index Swarm intelligence |
| Content Type | Text |
| Resource Type | Article |
