Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

Luo, Qifang; Zhang, Sen; Zhou, Yongquan

doi:10.1155/2017/1803934

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…For example, the problem of measuring the similarity between two overlapping sets of images is known as template matching. The work in [35] used stochastic fractal search to solve this problem, comparing the experimental results with algorithms, such as artificial bee colony and imperialist competitive algorithm, among others. The results showed that the algorithm obtained a better performance in contrast to other works present in the literature.…”

Section: State Of the Artmentioning

confidence: 99%

Stochastic Fractal Search Algorithm Improved with Opposition-Based Learning for Solving the Substitution Box Design Problem

2022

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The main component of a cryptographic system that allows us to ensure its strength against attacks, is the substitution box. The strength of this component can be validated by various metrics, one of them being the nonlinearity. To this end, it is essential to develop a design for substitution boxes that allows us to guarantee compliance with this metric. In this work, we implemented a hybrid between the stochastic fractal search algorithm in conjunction with opposition-based learning. This design is supported by sequential model algorithm configuration for the proper parameters configuration. We obtained substitution boxes of high nonlinearity in comparison with other works based on metaheuristics and chaotic schemes. The proposed substitution box is evaluated using bijectivity, the strict avalanche criterion, nonlinearity, linear probability, differential probability and bit-independence criterion, which demonstrate the excellent performance of the proposed approach.

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Section: State Of the Artmentioning

confidence: 99%

Stochastic Fractal Search Algorithm Improved with Opposition-Based Learning for Solving the Substitution Box Design Problem

2022

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“…SFS strategy has been successfully tested in various fields of science in the mono-objective context, such as:template matching (Luo et al 2017), mathematical functions (Zuo et al 2017), 3D protein structure prediction (Zhou et al 2018), distribution systems (Nguyen et al 2018), combined heat and power economic dispatch problem (Alomoush, 2019), economic load dispatch (Pham et al 2019), solar photovoltaic parameter estimation (Chen et al 2020), among other studies.…”

Section: Multi-objective Optimization Stochastic Fractal Searchmentioning

confidence: 99%

Solution of Fractional Optimal Control Problems by using orthogonal collocation and Multi-objective Optimization Stochastic Fractal Search

2021

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In this contribution the solution of Fractional Optimal Control Problems (FOCP) by using the Orthogonal Collocation Method (OCM) and the Multi-objective Optimization Stochastic Fractal Search (MOSFS) algorithm is investigated. For this purpose, three classical case studies on engineering are considered. Initially, the concentration profiles of laccase enzyme production process are analyzed to evaluate the influence of fractional order. Then, two classical FOCP (Catalyst Mixing and Batch Reactor) are solved by using the association between OCM and MOSFS approachesthrough the formulation and solution of a multi-objective optimization problem. The results indicate that the variation of the fractional order impliesdifferent values for the original objective function. In addition, physicallyincoherent profiles can be obtained by considering the fluctuation of the fractional order. Finally, the proposed MOSFS is considered as apromising methodology to solve multi-objective optimization problems.

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“…One of these algorithms is the stochastic fractal search (SFS) algorithm developed by Salimi [24]. The SFS algorithm has been applied to many optimization problems and has yielded successful results [25][26][27][28][29][30][31][32][33][34][35]. These algorithms can be used to solve complex computational optimization problems, however, fast convergence along with accuracy is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Stokastik Fraktal Arama Algoritması ve CMA-ES tabanlı yeni bir hibrit algoritma

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2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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In this study, a novel hybridization approach, which is called CMASFS and is based on the covariance matrix adaptation evolution strategy (CMA-ES) and the stochastic fractal search (SFS) algorithms. To make the proposed algorithm dynamic, Gaussian walk equations involved in the diffusion process of SFS have been updated and the algorithm decide to use which the Gaussian walk equations. The effectiveness of the proposed algorithm is tested using CEC2017 benchmark functions having unimodal, multimodal, hybrid, and composition functions in 10, 30, 50, and 100 dimensions. The performance of the CMASFS algorithm is compared with 17 metaheuristic algorithms given in the literature over the CEC2017 benchmark functions. According to the results, it is seen that CMASFS is generally obtained better mean error values. Moreover, to show the superiority of the proposed algorithm, Friedman analysis and the Wilcoxon rank-sum test are applied to the test results of the algorithms. The results of the Wilcoxon signed-rank test show that the improvement with the CMASFS algorithm is statistically significant on the majority of the CEC2017. The results of Friedman test verify that the CMASFS is obtained the best rank compared to both the original SFS and other compared algorithms.

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Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

Cited by 7 publications

References 24 publications

Stochastic Fractal Search Algorithm Improved with Opposition-Based Learning for Solving the Substitution Box Design Problem

Stochastic Fractal Search Algorithm Improved with Opposition-Based Learning for Solving the Substitution Box Design Problem

Solution of Fractional Optimal Control Problems by using orthogonal collocation and Multi-objective Optimization Stochastic Fractal Search

Stokastik Fraktal Arama Algoritması ve CMA-ES tabanlı yeni bir hibrit algoritma

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