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Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions

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Abstract

This paper proposes a novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm. Like other evolutionary algorithms, the proposed algorithm starts with an initial population. Population individuals are of two types: particles and hyper-sphere centers that all together form particle sets. Searching the hyper-sphere inner space made by the hyper-sphere center and its particle is the basis of the proposed evolutionary algorithm. The HSS algorithm hopefully converges to a state at which there exists only one hyper-sphere center, and its particles are at the same position and have the same cost function value as the hyper-sphere center. Applying the proposed algorithm to some benchmark cost functions shows its ability in dealing with different types of optimization problems. The proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO) and harmony search algorithm (HSA). The results show that the HSS algorithm has faster convergence and results in better solutions than GA, PSO and HSA.

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Correspondence to M. J. Sanjari.

Appendix

Appendix

Problem G1:

$$ \begin{gathered} f(x,y) = x\sin (4x) + 1.1y\sin (2y) \hfill \\ 0 < x,y > 10, {\text{ minimum:}} \, f(9.039,8.668) \hfill \\ \end{gathered} $$

Problem G2:

$$ \begin{gathered} f(x,y) = 0.5 + \frac{{\sin^{2} \sqrt {x^{2} + y^{2} } - 0.5}}{{1 + 0.1(x^{2} + y^{2} )}} \hfill \\ - 10 < x,y > 10, {\text{ minimum}}: \, f(0,0) = 0 \hfill \\ \end{gathered} $$

Problem G3:

$$ \begin{gathered} f(x,y) = - 0.01e^{{ - 0.2\sqrt {x^{2} + y^{2} } + 3(\cos 2x + \sin 2y)}} \hfill \\ - 5 < x,y > 5 \, ,{\text{ minimum}}:f(0,0) = - 3.4536 \hfill \\ \end{gathered} $$

Problem G4 (De Jong’s function):

$$ \begin{gathered} f(x,y) = 0.1(x^{2} + y^{2} ) \hfill \\ - 10 < x,y > 10 \, ,{\text{ minimum}}:f(0,0) = 0 \hfill \\ \end{gathered} $$

Problem G5 (Rosenbrock’s valley function):

$$ \begin{gathered} f(x,y) = 100(y - x^{2} )^{2} + (1 - x)^{2} \hfill \\ - 10 < x,y > 10 \, ,{\text{ minimum}}:f(0,0) = 0 \hfill \\ \end{gathered} $$

Problem G6 (Rastrigin’s function):

$$ \begin{gathered} f(x,y) = 0.1*(10*2 + (x^{2} - 10\cos (2\pi x)) + (y^{2} - 10\cos (2\pi y))) \hfill \\ - 10 < x,y > 10 \, ,{\text{ minimum}}:f(0,0) = 0 \hfill \\ \end{gathered} $$

Problem G7 (Griewangk’s function):

$$ \begin{gathered} f(x,y) = \frac{{x^{2} + y^{2} }}{4000} - \cos (x)\cos \left( {\frac{y}{\sqrt 2 }} \right) + 1 \hfill \\ - 10 < x,y > 10 \, ,{\text{ minimum}}:f(0,0) = 0 \hfill \\ \end{gathered} $$

See Figs. 17, 18, 19, 20, 21 and 22.

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3D plot of function in problem G 2

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3D plot of function in problem G 3

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3D plot of function in problem G 4

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3D plot of function in problem G 5

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3D plot of function in problem G 6

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figure 22

3D plot of function in problem G 7

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Karami, H., Sanjari, M.J. & Gharehpetian, G.B. Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions. Neural Comput & Applic 25, 1455–1465 (2014). https://doi.org/10.1007/s00521-014-1636-7

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