Survey of Robot 3D Path Planning Algorithms

Table 5

Analysis of bioinspired algorithms.

Method type	⁢Shortcoming		Advantages
	Shortcomings	Improvement
GA	High time complexity	[68, 69]	Able to solve NP-hard and multiobjectives problems
	Premature convergence	[70–72]
ACO	High time complexity	[7, 73, 74]	Able to deal with multiobjectives and continuous planning problems
PSO	High time complexity	[75]	It acts faster than GA and can deal with a low number of individuals problems
	Premature convergence
	Parameter sensitive	[76]
SFLA	High time complexity	[77]	It is more efficient than PSO and can achieve global convergence faster
	Parameter sensitive	[78]
MA	High time complexity	[79]	It is more efficient than GA in path smoothness and with low computational complexity
NN	High time complexity	[80–82]	Stable under sudden changes in the network
	Relying on suitable rules and organisms	[83]