Table 21 Parameters of comparison algorithms.
From: A novel meta-heuristic algorithm based on candidate cooperation and competition
Algorithm | Parameters | Description | Value |
|---|---|---|---|
PSO | \(c_1\) | Cognitive constant | 2.2 |
| Â | \(c_2\) | Social constant | 0.6 |
|  | w | Linear weight | [0.25, 0.95] |
FA | \(I_0\) | Original light intensity | 1 |
| Â | \(\beta _0\) | Initial attractiveness | 1 |
| Â | \(\gamma\) | Light absorption coefficient | \(\frac{1}{\sqrt{dimension}}\) |
CSA | AP | Awareness probability | 0.1 |
| Â | fl | Flight speed | 1.2 |
HMS | \(\alpha\) | The control factor | 0.01 |
| Â | K | The number of clusters | 5 |
| Â | C | Control coefficient | 2 |
ICA | impNum | The number of imperialist | 10 |
| Â | \(\epsilon\) | Influence of colony on overall imperialist | 0.1 |
| Â | \(\beta\) | Control the distance between colony and imperialist | 2 |
| Â | \(p_{re}\) | Revolution rate | 0.3 |
| Â | \(\gamma\) | Adjusting the deviation from the original direction | \(\frac{\pi }{4}\) |
BSO | \(\omega _1\) | Weight of selected solution | 0.5 |
| Â | \(\omega _2\) | Weight of another selected solution | 0.5 |
| Â | K | The number of clusters | 5 |
| Â | k | The slope of the logsig function | 1.2 |
| Â | \(p_{center}\) | Probability of selecting a cluster centre | 0.25 |
| Â | \(p_{cluster}\) | Probability of selecting one or two cluster centres | 0.5 |
