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| Category | Name | Advantages | Applications | References |
|
| Inspired from organism behavior | Bacterial Foraging Algorithm | Parameter insensitivity; strong robustness; easy implementation | Image segmentation; robot path planning; optimum scheduling; optimal power flow | [11–15, 53] |
| Monkey Climbing Algorithm | A few parameters to adjust; low calculation cost; fast convergence rate | Optimal sensor placement; feature selection and extraction; numerical optimization | [16–19, 54] |
|
| Inspired from organism structure | DNA Computing | High parallelism; massive storage ability; low energy consumption | Information security; robotic control; task assignment problem; clustering problem | [20, 25–28, 55] |
| Membrane Computing | Inherent parallelism; distributed feature; nondeterminism | Numerical optimization; broadcasting problem; computer graphics; traveling salesman problem | [21, 29–34] |
| Artificial Immune System | Noise patience; learning without teacher; self-organization and identity | Community detection; anomaly detection; fault diagnosis; web page classification | [5, 22, 35–41] |
|
| Inspired from evolution | Selfish Gene Algorithm | High convergent reliability and convergent velocity | Optimization design problem; traveling salesman problem; scheduling problem | [42–45, 57] |
| Invasive Weed Algorithm | Easy to understand; good adaptability; strong robustness | Image clustering problem; parameter estimation problem; numerical optimization | [46–48, 58, 59] |
| Culture Algorithm | With a double evolution structure; high search efficiency; with a certain universality | Pattern recognition; multirobot coordination; fault classification; engineering design problem | [5, 49–52, 60] |
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