Table 10.

Comparative review of TOA and compared algorithms.

Algorithm	Advantage	Disadvantage
GA	Good global search, simplicity, and comprehensibility	High memory consumption, control parameters, and poor local search.
PSO	Simplicity of the relationship and its implementation.	Control parameters, poor convergence, and entrapment in local optimum areas.
GSA	Easy implementation, fast convergence in simple problems, and low computational cost.	High computation, time consuming, several control parameters, and poor convergence in complex objective functions.
TLBO	Good global search, simplicity, and no requirement for any parameters	Poor convergence rate.
GWO	Fast convergence due to continuous reduction of search space, fewer storage and computational requirements, and easily implemented due to its simple structure.	Low convergence speed, poor local search, and low accuracy in solving complex problems.
WOA	Simple structure, fewer required operators, and appropriate balance between exploration and exploitation.	Low accuracy, slow convergence, and easily falls into local optimum.
MPA	Good global search and fast convergence.	High computation, time consuming, and control parameters.
TSA	Fast convergence, good global search, and appropriate balance between exploration and exploitation.	Poor convergence, control parameters, and falling into local optimal solutions when solving high-dimensional multimodal problems.
TOA	Simplicity of equations, easy implementation, lack of control parameters, proper exploitation, proper exploration, not caught up in local optimal solutions, and high convergence power.	The important thing about all optimization algorithms is that it cannot be claimed that one particular algorithm is the best optimizer for all optimization problems. It is also always possible to develop new optimization algorithms that can provide more desirable quasi-optimal solutions that are also closer to the global optimal.