A selection method for evolutionary algorithms based on the Golden Section

Cuevas, Erik; Enríquez, Luis Aguilera; Zaldívar, Daniel; Pérez‐Cisneros, Marco

doi:10.1016/j.eswa.2018.03.064

Cited by 23 publications

(8 citation statements)

References 24 publications

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“…By minimizing f (h) in equation (11), h opt can be obtained using such search algorithms as the golden section search method [42]. The minimization of f (h) involves the computation of ∂E(v) ∂var…”

Section: B Minimum Mse Based Decay Parameter Adaptation 1) Minimizatmentioning

confidence: 99%

Nonlocal Means Image Denoising With Minimum MSE-Based Decay Parameter Adaptation

Zhan

Ding

et al. 2019

IEEE Access

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Nonlocal means (NLM), a patch-based nonlocal recovery paradigm, has attracted much attention in recent decades. The decay parameter will greatly affect restoration performance of the NLM method. However, the existing NLM methods with decay parameter adaptation cannot determine this parameter effectively. To address this problem, we have proposed the minimum mean square error (MSE) based decay parameter adaptation method for the NLM denoising. In the proposed method, the globally optimal decay parameter is determined to produce the pre-denoised image based on the derived relation between the global minimum MSE and the decay parameter. Then, the pixel-wise MSE is estimated based on the pre-denoised result and the corresponding method noise. Finally, the optimal pixel-wise decay parameter is obtained by minimizing the pixel-wise MSE to produce the estimated restored image and the boosting strategy is implemented on this image to generate the final denoised result. Extensive simulations on the standard test images and real images corrupted with Gaussian noise and speckle noise demonstrate that the proposed method significantly outperforms some compared NLM methods in noise reduction and detail preservation and can provide better restoration performance than other state-of-the-art denoising methods in most cases in terms of objective metrics and human vision. INDEX TERMS Nonlocal means, decay parameter, mean square error, Gaussian noise, speckle noise. u(x, y) = NL[I ](x, y) = (p,q)∈ I (p, q)ω(x, y, p, q) (p,q)∈ ω(x, y, p, q) , (1) where NL denotes the nonlocal means, and is a search window. The weight ω(x, y, p, q) denoting the similarity between two pixels at (p, q) and (x, y) is calculated as: ω(x, y, p, q) = exp − ||I (P(p, q)) − I (P(x, y))|| 2 2,a h 2 , (2) where h denotes the decay parameter controlling the filtering degree, P(p, q) and P(x, y) are the two similarity windows (image patches) centered at (x, y) and (p, q), respectively; ||I (P(p, q)) − I (P(x, y))|| 2 2,a means the weighted Euclidean distance between two image patches P(p, q) and P(x, y) of

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Section: B Minimum Mse Based Decay Parameter Adaptation 1) Minimizatmentioning

confidence: 99%

Nonlocal Means Image Denoising With Minimum MSE-Based Decay Parameter Adaptation

Zhan

Ding

et al. 2019

IEEE Access

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“…This algorithm uses linear minimum mean square error (LMMSE) estimation criterion 55 to combine C2V message magnitude of both SPA and MSA, respectively. Then, this estimated parameters are determined using a popular search technique known as Golden‐Section Search (GSS) 56 . The decoding steps of this scheme are illustrated as

M_{1} = ψ ((), \sum_{n \in N ((), m) \ n} ψ (||, β_{mn'}^{((), i - 1)})),

M_{2} = \min_{n \in N ((), m) \ n} ((), |β_{italicmn'}^{(i - 1)}|) .

Let M 1 and M 2 indicate the check to variable node message broadcasting magnitude of the BPA and MSA.…”

Section: Min‐sum Approximation‐based Decoding Algorithmsmentioning

confidence: 99%

Certain investigations on recent advances in the design of decoding algorithms using low‐density parity‐check codes and its applications

Roberts¹,

Kumari

Anguraj³

2021

Int J Communication

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Summary Information theory coding is an impressive and most celebrated field of research that has spawned numerous extremely important solutions to the intractable problems of secure data communications. Recent advancements in error control coding methods have seen a huge surge in using low‐density parity‐check (LDPC) code‐based decoding algorithms to solve imperative issues related to reliable data transmission and reception. Till date, extensive research efforts have been consistently being made on LDPC codes which focus on algorithm‐driven and hardware‐realization‐based approaches. The main intension of this research work is to provide an extensive systematic elucidation on the recent advancements in LDPC decoding algorithms. In addition, a thorough performance evaluation and analysis of several outstanding LDPC decoding techniques is presented. Finally, conclusions are drawn by summarizing the important research findings, interesting open problems, current challenges and broader perspectives for future directions of research.

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“…e core framework of this method is to divide the space into several sections or groups. e main goal is to separate the space using this region division method in a way that allows for better functionality, spacing, and distribution [22]. It is well known that the feasible or theoretical optimal solution of the optimization problem is distributed in a certain interval of its solution space.…”

Section: E Golden Section (Gs) Can Guide the Solutions To Search For mentioning

confidence: 99%

Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization

Jia

Yun

et al. 2019

Scientific Programming

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Dynamically dimensioned search (DDS) is a well-known optimization algorithm in the field of single solution-based heuristic global search algorithms. Its successful application in the calibration of watershed environmental parameters has attracted researcher’s extensive attention. The dynamically dimensioned search algorithm is a kind of algorithm that converges to the global optimum under the best condition or the good local optimum in the worst case. In other words, the performance of DDS is easily affected by the optimization conditions. Therefore, this algorithm has also suffered from low robustness and limited scalability. In this work, an improved version of DDS called DDS-POBL is proposed. In the DDS-POBL, two effective methods are applied to improve the performance of the DDS algorithm. Piecewise opposition-based learning is introduced to guide DDS search in the right direction, and the golden section method is used to search for more promising areas. Numerical experiments are performed on a set of 23 classic test functions, and the results represent significant improvements in the optimization performance of DDS-POBL compared to DDS. Several experimental results using different parameter values demonstrate the high solution quality, strong robustness, and scalability of the proposed DDS-POBL algorithm. A comparative performance analysis between the DDS-POBL and other powerful algorithms has been carried out by statistical methods by using the significance of the results. The results show that DDS-POBL works better than PSO, CoDA, MHDA, NaFA, and CMA-ES and gives very competitive results when compared to INMDA and EEGWO. Moreover, the parameter calibration application of the Xinanjiang model shows the effectiveness of the DDS-POBL in the real optimization problem.

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A selection method for evolutionary algorithms based on the Golden Section

Cited by 23 publications

References 24 publications

Nonlocal Means Image Denoising With Minimum MSE-Based Decay Parameter Adaptation

Nonlocal Means Image Denoising With Minimum MSE-Based Decay Parameter Adaptation

Certain investigations on recent advances in the design of decoding algorithms using low‐density parity‐check codes and its applications

Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization

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