scholarly journals SAR IMAGE COMPRESSION USING ADAPTIVE DIFFERENTIAL EVOLUTION AND PATTERN SEARCH BASED K-MEANS VECTOR QUANTIZATION

2018 ◽  
Vol 37 (1) ◽  
pp. 35 ◽  
Author(s):  
Karri Chiranjeevi ◽  
Umaranjan Jena
Keyword(s):  
Differential Evolution ◽  
Vector Quantization ◽  
Signal To Noise Ratio ◽  
Search Space ◽  
Pattern Search ◽  
Lbg Algorithm ◽  
Search Optimization ◽  
Fitness Value ◽  
Adaptive Differential Evolution ◽  
Approximation Coefficients

A novel Vector Quantization (VQ) technique for encoding the Bi-orthogonal wavelet decomposed image using hybrid Adaptive Differential Evolution (ADE) and a Pattern Search optimization algorithm (hADEPS) is proposed. ADE is a modified version of Differential Evolution (DE) in which mutation operation is made adaptive based on the ascending/descending objective function or fitness value and tested on twelve numerical benchmark functions and the results are compared and proved better than Genetic Algorithm (GA), ordinary DE and FA. ADE is a global optimizer which explore the global search space and PS is local optimizer which exploit a local search space, so ADE is hybridized with PS. In the proposed VQ, in a codebook of codewords, 62.5% of codewords are assigned and optimized for the approximation coefficients and the remaining 37.5% are equally assigned to horizontal, vertical and diagonal coefficients. The superiority of proposed hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) optimized vector quantization over DE is demonstrated. The proposed technique is compared with DE based VQ and ADE based quantization and with standard LBG algorithm. Results show higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similiraty Index Measure (SSIM) indicating better reconstruction. 

scholarly journals Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1565 ◽  
Author(s):  
Xingping Sun ◽  
Linsheng Jiang ◽  
Yong Shen ◽  
Hongwei Kang ◽  
Qingyi Chen
Keyword(s):  
Differential Evolution ◽  
Clustering Analysis ◽  
Numerical Optimization ◽  
Optimization Algorithms ◽  
Search Space ◽  
Population Diversity ◽  
Convergence Of Algorithms ◽  
Adaptive Differential Evolution ◽  
Complex Methods ◽  
Single Objective

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.

Design of a Parallel Manipulator Based on Multi-Objective Self-Adaptive Differential Evolution Algorithm

2013 ◽  
Vol 328 ◽  
pp. 3-8 ◽  
Author(s):  
Qing Mei Meng
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Search Space ◽  
Parallel Robot ◽  
Feasible Region ◽  
Multi Objective ◽  
Trial Vector ◽  
Adaptive Differential Evolution ◽  
Optimal Pid Control ◽  
Self Adaptive

In order to improve highly non-isotropic input-output relations in the optimal design of a parallel robot, this paper presents a method based on a multi-objective self-adaptive differential evolution (MOSaDE) algorithm.The approach considers a solution-diversity mechanism coupled with a memory of those sub-optimal solutions found during the process. In theMOSaDE algorithm, both trial vector generation strategies and their associated control parameter values were gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings could be determined adaptively to match different phases of the search processevolution.Furthermore, a constraint-handling mechanism is added to bias the search to the feasible region of the search space. The obtained solution will be a set of optimal geometric parameters and optimal PID control gains. The empirical analysis of thenumerical results shows the efficiency of the proposed algorithm.

Genetic Simulated Annealing-Based Kernel Vector Quantization Algorithm

2017 ◽  
Vol 31 (05) ◽  
pp. 1758002 ◽  
Author(s):  
Mengling Zhao ◽  
Xinyu Yin ◽  
Huiping Yue
Keyword(s):  
Simulated Annealing ◽  
Vector Quantization ◽  
Signal To Noise Ratio ◽  
Optimal Solution ◽  
Point Of View ◽  
Local Optimum ◽  
Original Image ◽  
Crossover Operator ◽  
Codebook Design ◽  
Lbg Algorithm

Genetic Algorithm (GA) has been successfully applied to codebook design for vector quantization and its candidate solutions are normally turned by LBG algorithm. In this paper, to solve premature phenomenon and falling into local optimum of GA, a new Genetic Simulated Annealing-based Kernel Vector Quantization (GSAKVQ) is proposed from a different point of view. The simulated annealing (SA) method proposed in this paper can approach the optimal solution faster than the other candidate approaches. In the frame of GA, firstly, a new special crossover operator and a mutation operator are designed for the partition-based code scheme, and then a SA operation is introduced to enlarge the exploration of the proposed algorithm, finally, the Kernel function-based fitness is introduced into GA in order to cluster those datasets with complex distribution. The proposed method has been extensively compared with other algorithms on 17 datasets clustering and four image compression problems. The experimental results show that the algorithm can achieve its superiority in terms of clustering correct rate and peak signal-to-noise ratio (PSNR), and the robustness of algorithm is also very good. In addition, we took “Lena” as an example and added Gaussian noise into the original image then adopted the proposed algorithm to compress the image with noise. Compared to the original image with noise, the reconstructed image is more distinct, and with the parameter value increasing, the value of PSNR decreases.

OPTIMIZATION OF PLATE-FIN HEAT EXCHANGER USING ADAPTIVE DIFFERENTIAL EVOLUTION JADE

2016 ◽  
Author(s):  
Marcos Batistella Lopes ◽  
Viviana Mariani ◽  
Emerson Hochsteiner de Vasconcelos Segundo
Keyword(s):  
Heat Exchanger ◽  
Differential Evolution ◽  
Adaptive Differential Evolution
Sign in / Sign up

Export Citation Format

Share Document