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A space transformational invasive weed optimization for solving fixed-point problems

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Abstract

Real life problems are used as benchmarks to evaluate the performance of existing, improved and modified evolutionary algorithms. In this paper, we propose a new hybrid method, namely SIWO, by embedding space transformation search (STS) into invasive weed optimization to solve complex fixed-point problems. Invasive weed optimization suffers from premature convergence when solving complex optimization problems. Using STS transforms the current search space into a new search space by simultaneously evaluating solutions in the current and transformed spaces. This increases the probability that a solution is closer to the global optimum. Therefore, we can avoid premature convergence and the convergence speed is also increased. To evaluate the performance of SIWO, four complex fixed-point problems are chosen from the literature. Our findings demonstrate that SIWO can solve complex fixed-point problems with great precision. Moreover, the numerical results demonstrate that SIWO is an effective and efficient algorithm compared with some state-of-the-art algorithms.

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Authors and Affiliations

  1. School of Basic Sciences, Indian Institute of Technology, Bhubaneswar, India

    Y. Ramu Naidu & A. K. Ojha

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  1. Y. Ramu Naidu
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  2. A. K. Ojha
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Correspondence to Y. Ramu Naidu.

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Naidu, Y.R., Ojha, A.K. A space transformational invasive weed optimization for solving fixed-point problems. Appl Intell 48, 942–952 (2018). https://doi.org/10.1007/s10489-017-1021-1

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