Black-box optimization on BBOB suite d=30 (Subset Metrics) benchmark leaderboard · SOTA2 Research

Black-box optimization on BBOB suite d=30 (Subset Metrics)

32.5F4 Objective ValueCMAES

Evaluation Results

Method	Links
CMAES2026.01		32.5	5.33	326,000	563	24.8	8,990,000	35,500	1.03	1.09	3.99	3,360,000,000,000	6.76	-15.3	86.6	3.3	258
ABOM2026.01		545	260	558	115	2,350	972,000	26,100	52,800,000	72.8	0.0346	512	0.248	-65.7	86.6	0.301	244
JDE212026.01		5,580	28,000	3,860	7,850	23,400	17,300,000	73,200	1,520,000,000	518	372	561	20.1	-62.9	86.6	3.32	570
RLDEAFL2026.01		6,180	20,900	6,280	1,040	43,600	7,420,000	85,700	645,000,000	771	194	521	28.7	-52	86.6	3.31	708
DE2026.01		9,840	61,200	24,500	1,270	15,600	111,000,000	104,000	1,050,000,000	696	3,760	603	12.2	-38.8	86.6	3.18	323
GLEET2026.01		36,800	37,600	8,110	12,900,000	20,300	15,400,000	32,300	39,600,000,000	3,570	569	579	13.7	-39.7	86.6	2.98	398
PSO2026.01		128,000	249,000	67,000	1,230,000,000	886,000,000	143,000,000	88,200	138,000,000,000	21,700	5,770	943	78,200	979,000	86.6	3.23	38,400
SAHLPSO2026.01		204,000	5,110,000	56,700	246,000,000	38,200,000	251,000,000	99,000	3,460,000,000,000,000,000	15,100	5,070	889,000	2,920	4,040	86.6	3.41	12,200
RS2026.01		517,000	54,200,000	268,000	14,500,000,000	11,900,000,000	747,000,000	112,000	1,680,000,000,000,000	43,400	35,200	142,000	1,010,000	12,400,000	86.6	3.29	146,000
GLHF2026.01		696,000	66,100,000	259,000	4,020,000,000	185	669,000,000	67,200	256,000,000,000,000,000	37,800	33,000	518,000	9.19	-4.1	86.6	3.34	61,100
LES2026.01		1,810,000	80,100,000	354,000	3,820,000,000	1,490	1,810,000,000	899,000	82,300,000,000,000,000,000	39,700	99,900	2,480,000	1,300	1,490	1,190	1,300	53,500