Stochastic Fractal Search: A powerful metaheuristic algorithm

Salimi, Hamid

doi:10.1016/j.knosys.2014.07.025

Cited by 531 publications

(290 citation statements)

References 47 publications

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“…Otherwise, calculate the absolute rate of decrease in mse between the best obtained so far minimum mse (bm) shown in (9) and the current iteration minimum mse (minm) shown in (10). If the rate is less or equals a predefined tolerance value (tol) as in (11), then increase a global stack (st g ) counter by one.…”

Section: Grass Roots Algorithm Mathematical Modelmentioning

confidence: 99%

“…In order to test the proposed metaheuristic GRA, seven standard test functions will be used. The obtained results will be compared with other nine well-known, and recently proposed algorithms which are Particle Swarm Optimization (PSO) [4], Differential Evolutionary algorithm (DEA) [5], Bee Colony Optimization (BCO) [6], Cuckoo Search Algorithm (CSO) [7], Wind Driven Algorithm (WDA) [8], Stochastic Fractal Search (SFS) [9], Symbiotic Organisms Search (SOS) [10], Grey Wolf Optimizer (GWO) [11], and Novel Bat Algorithm (NBA) [12]. This work proposes and implements a general grass root optimization algorithm GRA, comparing it with other meta-heuristic algorithms through using a variety of test function to evaluate the average mean absolute error, average number of effective iteration, and average effective processing time.…”

Section: Introductionmentioning

confidence: 99%

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Grass Fibrous Root Optimization Algorithm

Akkar¹,

Mahdi²

2017

IJISA

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Abstract-This paper proposes a novel meta-heuristic optimization algorithm inspired by general grass plants fibrous root system, asexual reproduction, and plant development. Grasses search for water and minerals randomly by developing its location, length, primary root, regenerated secondary roots, and small branches of roots called hair roots. The proposed algorithm explore the bounded solution domain globally and locally. Globally using the best grasses survived by the last iteration, and the root system of the best grass obtained so far by the iteration process and locally uses the primary roots, regenerated secondary roots and hair roots of the best global grass. Each grass represents a global candidate solution, while regenerated secondary roots stand for the locally obtained solution. Secondary generated hair roots are equal to the problem dimensions. The performance of the proposed algorithm is tested using seven standard benchmark test functions, comparing it with other metaheuristic well-known and recently proposed algorithms.

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Section: Grass Roots Algorithm Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grass Fibrous Root Optimization Algorithm

Akkar¹,

Mahdi²

2017

IJISA

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“…It can save compression time with degradation in image quality with respect to H.264. In metaheuristic algorithm [23], diffusion property in random fractals is used.…”

Section: Literature Reviewmentioning

confidence: 99%

Fractal based Image Compression Techniques

Kadam¹,

Rathod²

2017

IJCA

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Fractal image compression offers high compression ratios and quality image reconstruction. It uses various techniques as the fractal with DCT, wavelet, neural network, genetic algorithms, quantum acceleration etc. Additionally, because fractals are infinitely magnifiable, fractal compression is resolution independent and so a single compressed image can be used efficiently for display in any image resolution including resolution higher than the resolution of the original image. Breaking an image into pieces and identifying selfsimilar ones is the main principle of the approach. In this paper, the different issues in fractal image compression as partitioning, larger encoding time, compression ratio, quality of the reconstructed image, decoding time, SSIM(Structured Similarity Index) are discussed and highlighted. The various areas for improvement as larger encoding time and PSNR are also suggested. The various parameters for evaluating the performance of these techniques as PSNR, compression ratio, encoding time, and decoding time are also suggested. Comparison of Fractal techniques for color image, texture and satellite image is done using different parameters as compression time, compression ratio and PSNR. The hybrid method which combines Fractal quad tree with wavelet and Huffman coding is implemented and compared different parameters as compression ratio and the compression time of the proposed method with the existing methods.

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“…These algorithms are inspired from different things such as natural phenomena, natural selections and social behaviors and applied in solving the optimization problems. Examples of the recently metaheurtistc algorithms are Vortex search [4], WOA (whale optimization algorithm) [5], MBA (mine blast algorithm) [6], WCA (water cycle algorithm) [10], and SFS (stochastic fractal search) [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization

Sarbazfard¹,

Jafarian²

2016

ijacsa

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Abstract-In this paper, a new and an effective combination of two metaheuristic algorithms, namely Firefly Algorithm and the Differential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Differential Evolution (DE) and Firefly Algorithm (FA). Firefly algorithm is the natureinspired algorithm which has its roots in the light intensity attraction process of firefly in the nature. Differential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are effective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each algorithm deficiencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and findings showed that HFADE is a more preferable and effective method in solving the highdimensional functions.

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Stochastic Fractal Search: A powerful metaheuristic algorithm

Cited by 531 publications

References 47 publications

Grass Fibrous Root Optimization Algorithm

Grass Fibrous Root Optimization Algorithm

Fractal based Image Compression Techniques

A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization

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