Table 9.

Advantages and disadvantages of SCA

Advantages	Disadvantages
Easy to implement in many different programming languages	Suffers from premature convergence in some real-world optimization problems
Simplicity, robustness, adaptability, flexibility, and scalability are fundamental features found	Parameter tuning in SCA
Combination with other meta-heuristics and techniques	No theoretical analysis of convergence properties
Small number of control parameters that need to be adjusted
Lower stuck in local optima
Reasonable execution time
Suitable for a wide variety of difficult optimization problems
Good solutions