Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization

Table 1

Description of benchmark functions.

Test function	Name	Type	Dim	Range	Optimum
	Sphere	US	30	[−100, 100]	0
	Schwefel 2.22	UN	30	[−10, 10]	0
	Schwefel 1.2	UN	30	[−100, 100]	0
	Schwefel 2.21	US	30	[−100, 100]	0
	Rosenbrock	UN	30	[−30, 30]	0
	Step	US	30	[−100, 100]	0
	Quartic	US	30	[−1.28, 1.28]	0
	Schwefel 2.26	MS	30	[−500, 500]	−418.9829D
	Rastrigin	MS	30	[−5.12, 5.12]	0
	Ackley	MS	30	[−32, 32]	8.8818e−16
	Griewank	MN	30	[−600, 600]	0
	Penalized	MN	30	[−50, 50]	0
	Penalized2	MN	30	[−50, 50]	0
	Foxholes	MS	2	[−65.53, 65.53]	0.998004
	Kowalik	MS	4	[−5, 5]	0.0003075
	Six hump camel back	MN	2	[−5, 5]	−1.03163
	Branin	MS	2	[−5, 10]×[0, 15]	0.398
	Goldstein price	MN	2	[−5, 5]	3
	Hartman 3	MN	3	[0, 1]	−3.8628
	Hartman 6	MN	6	[0, 1]	−3.32
	Langermann 5	MN	4	[0, 10]	−10.1532
	Langermann 7	MN	4	[0, 10]	−10.4029
	Langermann 10	MN	4	[0, 10]	−10.5364