Differential evolution algorithm with population knowledge fusion strategy for image registration

Sun, Yu; Li, Yaoshen; Yang, Yingying; Yue, Hongda

doi:10.1007/s40747-021-00380-3

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…Sun et al, 2018: In [ 89 ], the authors propose an algorithm derived from DE, called SGD-DE, designed to achieve better registration of remote sensing images. The proposed algorithm divides the population in niches (clusters) and allows different evolution strategies to be applied on each niche.…”

Section: Articlesmentioning

confidence: 99%

Evolutionary Image Registration: A Review

Cocianu

Uscatu²,

Stan³

2023

Sensors

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Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.

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Section: Articlesmentioning

confidence: 99%

Evolutionary Image Registration: A Review

Cocianu

Uscatu²,

Stan³

2023

Sensors

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“…Michal et al, utilizing GA, submitted a fast-robotic pencil drawing application [29]. Sun, Yu, et al, [30] propounded an image registration technique with the DE algorithm. Liu et al, recommended a collaborative dragonfly algorithm with a novel communication strategy, for the segmentation of multi-thresholding color images [31].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Multi-Threaded Distributed Evolutionary Algorithms for Image-Processing Applications

2023

IJIES

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Evolutionary algorithms (EAs) in the repository of the evolutionary computing (EC) paradigm are simplistic and stochastic in nature. The desirable characteristic of adaptive simplicity has sustained research community's rampant curiosity in EAs. Distributed evolutionary algorithmic (DEA) framework is an instinctive extension of EAs. This paper summarizes the design and implementation of a multi-threaded DEA (MTDEA) framework. Empirical analysis between a traditional DEA framework and a simulated MTDEA is presented. The MTDEA framework was outfitted with three plug-in algorithms to handle commonly occurring faults in distributed environments. The propriety of the MTDEA framework was validated in two customary applications -Image thresholding and Image reproduction. The thresholding and reproduction problems were addressed by incorporation of the differential evolution (DE) and genetic algorithm (GA) methods respectively, into the MTDEA framework. The performances of DE and GA routines were compared with their sequential counterpart frameworks. The MTDEA framework was evaluated for reliability and scalability on a high-performance computing (HPC) system.

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“…Large-scale global optimization (LSGO) problems involve a large number of variables [1], and the dimensions of the problem are usually greater than 1000. Compared with general optimization problems, the size of the search space increases exponentially as the number of dimensions increases, which is called the "curse of dimensionality" [2]. Traditional optimization algorithms make it difficult to obtain the global optimal solution of the problem [3].…”

Section: Introductionmentioning

confidence: 99%

Three Stages Recursive Differential Grouping for Large-Scale Global Optimization

Zheng,

Xu,

Chen

2023

IEEE Access

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Cooperative co-evolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems by using the "divide-and-conquer" method. However, the decomposition stage faces the challenges of either insufficient decomposition accuracy or extremely high computational cost to achieve correct decomposition. The significant amount of resources consumed during the decomposition stage greatly affects optimization. A decomposition method called Recursive Differential Grouping (RDG) has shown impressive results in solving large-scale continuous optimization problems. To improve the performance of RDG and reduce the resource consumption during decomposition, this paper proposes the Three Stages Recursive Differential Grouping (TSRDG) method. The first stage is the determination of whether a function is fully separable or not. In the second stage, separable variables are divided into one group and non-separable variables are divided into another group. In the third stage, this study identifies the interacting decision variables that are not in a separable group and reuses the effective information that was gained in the first two stages. Compared with some state-of-the-art methods, TSRDG has an effective strategy for decomposing functions. Moreover, it avoids the resource consumption of identifying the interaction between separable and non-separable variables in recursions. Effective historical information is fully exploited throughout the process of variable decomposition. Simulation experiments on the benchmark functions of CEC'2010 and CEC'2013 demonstrate that TSRDG achieves higher decomposition accuracy and lower computational cost than state-of-the-art decomposition methods. The experiments show that TSRDG is a promising algorithm in LSGO. INDEX TERMSCooperative co-evolution (CC), fully separable, large-scale global optimization (LSGO), recursive differential grouping

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Differential evolution algorithm with population knowledge fusion strategy for image registration

Cited by 12 publications

References 43 publications

Evolutionary Image Registration: A Review

Evolutionary Image Registration: A Review

Performance Analysis of Multi-Threaded Distributed Evolutionary Algorithms for Image-Processing Applications

Three Stages Recursive Differential Grouping for Large-Scale Global Optimization

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