Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Chakraborty, Subhankari Prasad; Saha, Apu Kumar; Ezugwu, Absalom E.; Agushaka, Jeffrey O.; Zitar, Raed Abu; Abualigah, Laith

doi:10.1007/s11831-022-09825-5

Cited by 28 publications

(9 citation statements)

References 241 publications

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“…Another classic algorithm is CS; this is a bio-inspired algorithm on parasite nurseries of certain types of cuckoo birds; it is widely applied due to its ease of adaptation to different problems; it has also been modified on a large number of occasions [51]- [53]. One of the most potent and versatile evolutionary optimizers is the differential evolution algorithm, which, despite being more than two decades old, is used in various applications [54], [55] and continues to attract researchers to design modifications that improve its performance [56], [57]. The Grey Wolf Optimizer has been extensively used for a wide range of optimization problems due to its remarkable advantages over other swarm intelligence algorithms: it is easy to use, scalable, and has the unique ability to balance the exploration and exploitation in a way that promotes a favorable convergence during the search process [58].…”

Section: Metaheuristicsmentioning

confidence: 99%

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Miguel-Andrés,

Ramos-Frutos,

Sharawi

et al. 2024

IEEE Access

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Musculoskeletal disorders of the foot are a common complaint in the population. It has been found a flatfoot prevalence of 13.6% in young adults and a prevalence of 26.62% in adults between 42 and 91 years. Different non-invasive techniques can identify the type of foot by analyzing the soles of the feet, such as the Chippaux-Smirak index (CSI). Although CSI is a non-invasive technique, it is performed manually, and the intervention of an expert is necessary to give a clinical opinion. The use of automatic systems is an alternative. This article introduces a machine learning-based tool that permits the identification of foot types. The proposal employs a wrapper feature selection mechanism to select the subset of features that improves the classification. This task is considered from an optimization perspective, and the optimal subset is chosen using metaheuristic algorithms. Eight algorithms used in the optimization are compared, and an increase in the Accuracy of the K-nearest neighbors (KNN) classifier is observed from 73.5% to 94.7%. Of the 39 total features proposed in the dataset, only 10 features are considered significant. The significance of the characteristics implies that they have an effect on the morphology of the foot. If they are considered in treatments to minimize this disease, it can reduce their development costs.

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Section: Metaheuristicsmentioning

confidence: 99%

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Miguel-Andrés,

Ramos-Frutos,

Sharawi

et al. 2024

IEEE Access

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“…Once all trial vectors are produced, a greedy selection between each couple of target vectors and its corresponding trial vector is applied [13]. Since its introduction in 1995 by Storn and Kenneth [35], the DE algorithm has become an essential method for a large number of real problems or benchmarks, such as engineering domains [34], image processing [36], system parameters identification [37], robust control of nonlinear systems [38], and power scheduling problems [39][40][41]. In the aforementioned references, the performance and effectiveness of DE-based methods have been investigated and evaluated, and it has been shown that DE can perform better than other well-known population-based techniques such as GA and PSO.…”

Section: Multi Objectivementioning

confidence: 99%

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

2023

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The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).

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“…Its straight forward execution, simple and small structure, and quick convergence can be considered the main reasons for its great efficiency. It has been successfully applied to a wide range of real-life applications, such as image processing [18,19], industriel noise recognition [20], bit coin price forecasting [21], optimal power flow [22], neural network optimization [23], engineering design problems [24], and so on. There are also several other fields like controlling theory [25,26] which are also open for the application of the DE algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics

Kumar,

Ali

2024

Biomimetics

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The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as “Improved DE with Best and Worst positions (IDEBW)”, offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient.

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Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Cited by 28 publications

References 241 publications

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Wrapper-Based Feature Selection to Classify Flatfoot Disease

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics

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