Stochastic Fractal Search Algorithm and its Application in Path Planning

Li, Wenguang; Sun, Shiyu; Li, Jianzeng; Hu, Yue

doi:10.1109/gncc42960.2018.9018694

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…The sample number of the power of the PV module, Kyocera KC200GT-215, N, is 34. Sine map-based update by using (31) and (32) Furthermore, Table VI also shows the difference between the SFS and ISFS algorithms in the solution initialization, the procedure of creating a new solution and the procedure of updating the solution. These chaotic map-based modifications are to create variety in the search space of the solutions for enhancing the exploitation and exploration abilities of the ISFS algorithm through the procedures of creating a new solution and updating the solution.…”

Section: Resultsmentioning

confidence: 99%

“…[31] Optimal path planning for UAVs [32] Optimization for the learning rate and the label smoothing regularization factor in a deep learning model of a convolutional neural network [33] Table IV shows the wide applicability of the SFS algorithm in many various fields. The SFS algorithm-based achievements confirm the effectiveness and superiority of the SFS algorithm with its ability to maintain the balance between exploration and exploitation as well as the ease of being used in optimization applications with fewer tuning parameters.…”

Section: Siaw-pso Algorithmmentioning

confidence: 99%

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Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

2022

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The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean squared error (RMSE) of the powers obtained from the PV module through estimation and experiment. The improved stochastic fractal search (ISFS) algorithm is proposed to solve this minimization with two modifications. The first replaces the logarithmic function with the exponential function in the standard deviation of the diffusion technique to improve the exploration ability efficiently in the search space. The second utilizes the sine map instead of the uniform distribution in both the diffusion and update techniques for improving the performance of the ISFS algorithm. Numerical results demonstrate the remarkable ability of the ISFS algorithm in obtaining both the model parameters and MPPs of the PV module with high accuracy. The comparison shows that the ISFS algorithm outperforms other meta-heuristic algorithms such as a stochastic fractal search (SFS) algorithm, a particle swarm optimization (PSO) algorithm, and an improved particle swarm optimization (IPSO) algorithm in the proposed parameter estimation application.INDEX TERMS Estimation, maximum power point, meta-heuristic algorithms, model parameter, photovoltaic modules

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Section: Resultsmentioning

confidence: 99%

Section: Siaw-pso Algorithmmentioning

confidence: 99%

Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

2022

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“…The work confirmed the ability of the algorithm to achieve values close to the global optimum within a short time frame. Stochastic fractal search was used to address the problem of unmanned aerial vehicle path planning, finding good results in acceptable times [37]. The problem of modeling photovoltaic systems includes the estimation of parameters with the available values of voltage and current.…”

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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Izgara Bazlı Yol Planlama için Matematik Tabanlı Metasezgisellerin Karşılaştırılması

Yildirim¹,

Akay²

2022

European Journal of Science and Technology

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ÖzRobot navigasyonunun en önemli bileşenlerinden biri olan yol planlama son yıllarda da araştırmacılar tarafından kapsamlı bir şekilde incelenmekte ve bu problem için birçok farklı metasezgisel algoritma kullanılmaktadır. Bu çalışmada ızgara tipi bir ortamda bir mobil robotun küresel yol planlaması ele alınmış ve bu problem için farklı matematik tabanlı metasezgisel algoritmalarının etkileri incelenmiştir. Öncelikle ızgara tipinde ve farklı zorluk derecelerinde üç farklı ortam tasarlanmıştır. Ardından, son yıllarda geliştirilen farklı matematik tabanlı algoritmalar kullanılarak robotun ortamlardaki optimum yolları hesaplanmıştır. Çalışmada metasezgisel algoritma olarak stokastik fraktal arama (Stochastic Fractal Search, SFS), aritmetik optimizasyon algoritması (Arithmetic Optimization Algorithm, AOA) ve sinüs kosinüs algoritması (Sine Cosine Algorithm, SCA) kullanılmıştır. Bulgular değerlendirildiğinde SFS algoritmasının en kısa mesafe ve engelden kaçınma açısından diğer algoritmalara göre daha iyi sonuçlar verdiği gözlemlenmiştir.

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Stochastic Fractal Search Algorithm and its Application in Path Planning

Cited by 4 publications

References 1 publication

Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

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

Izgara Bazlı Yol Planlama için Matematik Tabanlı Metasezgisellerin Karşılaştırılması

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