Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm

Table 2

Path planning results in environment 2.

Algorithms	Best fitness value	Mean fitness value	Worst fitness value	Average execution time	Standard deviation	Number of optimal iterations
DE	32.8655	33.8655	35.0157	12.3581	1.1001	329
GA	31.4532	32.4532	33.6302	5.6875	1.0895	50
SA	32.2260	35.9860	34.1927	26.1047	0.9834	396
PSO	30.0592	32.0592	31.8083	5.0907	0.8744	50
CAPSO	29.7213	29.9821	31.1723	4.7042	0.7255	44