Yazar "Orujpour, Mohanna" seçeneğine göre listele

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    Multi-level image thresholding based on social spider algorithm for global optimization
    (Springer Science+Business Media B.V., 2019) Rahkar, Farshi, Taymaz; Orujpour, Mohanna
    Thresholding is one of the simplest and popular technique for segmenting images. Maximum between-class variance (Otsu’s) method is one of the well-known and widely used method in case of segmentation. Not only Otsu could be used for bi-level thresholding but also it could be extended to multi-level image thresholding. Finding the optimum threshold values in multi-level case is very time consuming process, thus optimization algorithm can deal with this problem. In this paper social spider algorithm for global optimization has been used for maximizing the between-class variance to carry out multi-level image thresholding. Experimental outcomes have demonstrated that the proposed method is capable of estimating threshold values and yield satisfying outcome. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.
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    Öğe
    Multi-modal forest optimization algorithm
    (Springer London Ltd, 2020) Orujpour, Mohanna; Feizi-Derakhshi, Mohammad-Reza; Rahkar-Farshi, Taymaz
    Multi-modal optimization algorithms are one of the most challenging issues in the field of optimization. Most real-world problems have more than one solution; therefore, the potential role of multi-modal optimization algorithms is rather significant. Multi-modal problems consider several global and local optima. Therefore, during the search process, most of the points should be detected by the algorithm. The forest optimization algorithm has been recently introduced as a new evolutionary algorithm with the capability of solving unimodal problems. This paper presents the multi-modal forest optimization algorithm (MMFOA), which is constructed by applying a clustering technique, based on niching methods, to the unimodal forest optimization algorithm. The MMFOA operates by dividing the population of the forest into subpopulations to locate existing local and global optima. Subpopulations are generated by the Basic Sequential Algorithmic Scheme with a radius neighborhood. As population size is self-adaptive in MMFOA, population size can be increased in functions with too many local and global optima. The proposed algorithm is evaluated by a set of multi-modal benchmark functions. The experiment results show that not only is the population size low, but also that the convergence speed is high, and that the algorithm is efficient in solving multi-modal problems.