Success history intelligent optimizer

Fakhouri, Hussam N.; Hamad, Faten; Alawamrah, Abedalsalam

doi:10.1007/s11227-021-04093-9

Cited by 13 publications

(15 citation statements)

References 36 publications

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“…In the field of energy systems, with the growing focus on renewable sources, metaheuristics are being used to optimize wind farm layouts and manage energy distribution networks, as well as in load forecasting and energy consumption optimization 35 . Emerging trends in the field point towards the integration of metaheuristics with machine learning techniques, combining the robust optimization capabilities of metaheuristics with the data-driven power of machine learning 36 . This includes applications in feature selection for machine learning models and optimizing neural network architectures.…”

Section: Literature Reviewmentioning

confidence: 99%

Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

Hwaitat,

fakhouri,

Tahoun

et al. 2024

Preprint

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In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the Cognitive DL Retinal Blood Vessel Segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net Architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a Matched Filter (MF) for feature enhancement and a post-processing phase employing Morphological Techniques (MT) for refining the segmentation output. Also, the model incorporates Multi-Scale Line Detection and Scale Space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, Retinal Blood Vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

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Section: Literature Reviewmentioning

confidence: 99%

Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

Hwaitat,

fakhouri,

Tahoun

et al. 2024

Preprint

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“…These algorithms often apply forces or őeld-like effects to particles or solutions within the search space, driving them towards optimal conőgurations [24]. Invasive Weed Optimization (IWO) [35], Harmony Search (HS) [36], Light Spectrum Optimization (LSO) [37], and Pathőnder Algorithm (PFA) [38] further expand the spectrum of nature-inspired methodologies, showcasing the diverse ways in which natural and theoretical frameworks can be transformed into practical optimization tools [39].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, evolutionary and ecological processes have inspired algorithms like the Cuckoo Optimization Algorithm (COA) [48], which models the parasitic reproduction strategy of cuckoos, and the Genetic Algorithm (GA) [49], which utilizes the principles of natural selection. Differential Evolution (DE) [50] and the Memetic Algorithm (MA) [51] both incorporate evolutionary strategies but with unique modiőcations to enhance performance and solution quality [52].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The genesis of meta-heuristic algorithms can be traced back to the 1960s and 1970s, with the advent of strategies like Genetic Algorithms (GAs) [3] and Simulated Annealing (SA) [4], which drew inspiration from biological evolution and thermodynamics, respectively [5]. However, the term "meta-heuristic" was formally coined in the late 1980s and early 1990s and began to see widespread adoption in academic and research circles.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Four Vector Intelligent Metaheuristic andDE for Solving Complex and Engineering DesignOptimization Problems

fakhouri,

Ishtaiwi,

Makhadmeh

et al. 2024

Preprint

Self Cite

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Metaheuristic algorithms play a pivotal role in addressing complex and nonlinear optimization challenges. However, traditional optimizers often struggle to locate the global optimum in intricate problem spaces, necessitating the development of hybrid methodologies. This paper introduces FVIMDE, a cutting-edge hybrid optimization algorithm that amalgamates the innovative Four Vector Intelligent Metaheuristic (FVIM) with the proven robustness of Differential Evolution (DE). Designed to adeptly maneuver through the complex terrains of various optimization and engineering design problems, FVIMDE is tested and evaluated over three well-known benchmark suites—CEC2017, CEC2022, and a specially set of 50 benchmark functions. statistacel tests has been calculated including mean, standard deviation and the wilcoxon sum rank test. Further FVIMDE has been compared with state-of-art optimizers. Subsequent applications of FVIMDE to five intricate structural engineering challenges further validate its effectiveness. Comparative analyses against several leading optimization algorithms highlight the superior adaptability and robustness of FVIMDE, showcasing its exceptional performance and significant improvements over traditional methods in diverse optimization environments.

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“…Results show that metaheuristic optimization works well for one type of optimization issue but not another. For the aforementioned causes, it is important to create a more efficient optimization metaheuristic algorithm [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

Ryalat¹,

Fakhouri²,

Zraqou³

et al. 2023

IJACSA

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Data testing is a vital part of the software development process, and there are various approaches available to improve the exploration of all possible software code paths. This study introduces two contributions. Firstly, an improved version of the Multi-verse Optimizer called Testing Multi-Verse Optimizer (TMVO) is proposed, which takes into account the movement of the swarm and the mean of the two best solutions in the universe. The particles move towards the optimal solution by using a mean-based algorithm model, which guarantees efficient exploration and exploitation. Secondly, TMVO is applied to automatically develop test cases for structural data testing, particularly path testing. Instead of automating the entire testing process, the focus is on centralizing automated procedures for collecting testing data. Automation for generating testing data is becoming increasingly popular due to the high cost of manual data generation. To evaluate the effectiveness of TMVO, it was tested on various well-known functions as well as five programs that presented unique challenges in testing. The test results indicated that TMVO performed better than the original MVO algorithm on the majority of the tested functions.

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Success history intelligent optimizer

Cited by 13 publications

References 36 publications

Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

Hybrid Four Vector Intelligent Metaheuristic andDE for Solving Complex and Engineering DesignOptimization Problems

Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

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