Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

Table 2

Comparison of performance on SCH after 20000 FEs.

SCH		NSABC	MOABC	MOCLPSO	NSGA-II
Converge metric	Average	1.4112𝑒−004	1.2621𝑒−004	3.5428𝑒−004	2.1868𝑒−004
	Median	1.2137𝑒−004	1.2791𝑒−004	2.4166𝑒−004	2.0044𝑒−004
	Best	8.5338𝑒−005	8.8590𝑒−005	1.1154𝑒−004	1.2921𝑒−004
	Worst	3.1097𝑒−004	2.1045𝑒−004	1.2674𝑒−003	3.5484𝑒−004
	Std	6.4495𝑒−005	3.8892𝑒−005	3.4346𝑒−004	8.3707𝑒−005
Diversity metric	Average	3.5641𝑒−001	3.5804𝑒−001	4.1216𝑒−001	7.3525𝑒−001
	Median	3.5852𝑒−001	3.5641𝑒−001	3.6338𝑒−001	7.3941𝑒−001
	Best	3.0512𝑒−001	3.0112𝑒−001	2.7346𝑒−001	7.1568𝑒−001
	Worst	4.1034𝑒−001	3.9650𝑒−001	6.7878𝑒−001	7.5692𝑒−001
	Std	3.0259𝑒−002	2.9056𝑒−002	1.3514𝑒−001	1.4628𝑒−002