Table 1 Summary of related work
From: An improved artificial bee colony algorithm based on Bayesian estimation
References | Year | Advantages | Disadvantages | Classification |
|---|---|---|---|---|
Zhu et al. [24] | 2010 | Global best solution is added into movement equation | The good neighbor information is not considered | Movement-equation-based |
Akay and Karaboga [15] | 2012 | The new parameter modification rate is introduced | More effective MR is not considered | Parameter-based |
Li et al. [9] | 2014 | Fully utilize the convergence status within the iteration system | The convergence rate is lower | Path planning |
Kiran et al. [16] | 2015 | Control evolutionary strategy selection | May have high computational complexity | Parameter-based |
Cui et al. [19] | 2016 | A depth-first search framework is combined with ABC | DFS alone has limited performance improvement | Probability-based |
Durgut et al. [17] | 2018 | Adaptive operator selection and credit assignment rule are introduced accordingly | More effective operator selection schemes need to be developed | Parameter-based |
Yu et al. [28] | 2018 | Combine different factors for different problems | Premature is very likely | Movement-equation-based |
Chu et al. [21] | 2020 | New probability model with the rate of successful searches and linear weight is presented | The pertinence of probability selection is not considered | Probability-based |
Thilak et al. [12] | 2021 | Search strategies based on differential evolution and the method integrated chaotic and opposition learning are introduced | Value neighbor information is not considered | Movement-equation-based |