Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization

Table 1

Summary of the benchmark test functions used in the experiment of this work.

Function type	Function name	Definition	Search interval
Unimodal functions	Sphere		[−100, 100]	0
	Sum-square		[−10, 10]	0
	Schwefel’s 2.22		[−10, 10]	0
	Rotated hyperellipsoid		[−100, 100]	0
	Schwefel’s 2.21		[−100, 100]	0
	Rosenbrock		[−30, 30]	0
	Step		[−100, 100]	0
	Quartic		[−1.28, 1.28]	0
	Noise		[−1.28, 1.28]	0
	Sum-power		[−1, 1]	0
Multimodal functions	Rastrigin		[−5.12, 5.12]	0
	Ackley		[−32, 32]	0
	Griewank		[−600, 600]	0
	Levy		[−10, 10]	0
	Alpine		[−10, 10]	0
	Inverted cosine mixture		[−1, 1]	0
	Zakharov		[−5, 10]	0
	Pathological		[−100, 100]	0
	Levy and montalo		[−5, 5]	0
	Elliptic		[−100, 100]	0
	Easom		[−100, 100]	0
	Salomon		[−100, 100]	0
	Schaffer		[−100, 100]	0