Table 5 Comparison of seven intelligent optimization algorithms.
Algorithms | Advantage | Disadvantage | Application problem |
|---|---|---|---|
ACO | Strong global optimization capability, easy to combine with other algorithms | Convergence speed is slow, easy to fall into local optimum | Discrete, small-scale and combinatorial optimization problems |
DE | Simple model, few algorithm parameters | High dependence on initial conditions, easy to fall into local optimum | Continuous optimization problem |
GA | Simple model and few algorithm parameters | After evolution, easy to fall into the local optimum, the population diversity is poor | Nonlinear multi-objective optimization |
PSO | Fast convergence and simple model | Fall into the local optimum in the later stage, and easy to converge prematurely | Continuous optimization questions |
SA | Simple model, few algorithm parameters | High dependence on initial conditions | Continuous optimization problem |
GWO | High global search capability | Prone to stagnation, poor population diversity | Continuous optimization problem |
SSA | High search accuracy and robustness | The population diversity is reduced, and it is easy to fall into the local optimum | Continuous optimization problem and combinatorial optimization problem |
