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SVSA: a Semi-Vortex Search Algorithm for solving optimization problems

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Abstract

The Vortex Search Algorithm (VSA) is a single-based meta-heuristic algorithm inspired by the vortices created by stirred fluids. A VSA search creates nested vortices starting from an initial center (the middle of the boundaries), and the vortices are gradually scaled down. In each iteration, the center of a new vortex is created by searching around the previous center and creating a set of candidate solutions with a Gaussian distribution, selecting best of them and comparing it with previous center, in order to replace it. However, this strategy can become trapped in a local optimum, since the search space may not be explored properly by time passing and according to the structure of the many objective functions. Therefore, in this paper an improved version of the VSA is introduced. Actually, the proposed method is not necessarily limited to creating nested vortices; after employing the VSA, the search space is explored by a uniform distribution in each iteration, then the center of the vortex moves toward the best random solution. In this situation, if the shifted center is better than the previous center, then the center is transferred to the new point; in this way, the VSA runs at the shifted center in the next iteration. Therefore, the proposed method is Semi-VSA (SVSA) and its vortices are animated, not necessarily nested. Experiments on 50 benchmark optimization functions indicate that the SVSA is able to improve the VSA, especially when the VSA has been trapped in a local optimum.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Seyedeh Fatemeh Razavi

  2. School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran

    Hedieh Sajedi

Authors
  1. Seyedeh Fatemeh Razavi
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  2. Hedieh Sajedi
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Correspondence to Hedieh Sajedi.

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Razavi, S.F., Sajedi, H. SVSA: a Semi-Vortex Search Algorithm for solving optimization problems. Int J Data Sci Anal 8, 15–32 (2019). https://doi.org/10.1007/s41060-018-0154-6

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