An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer

Houssein, Essam H.; Helmy, Bahaa El-din; Oliva, Diego; Jangir, Pradeep; Premkumar, M.; Elngar, Ahmed A.; Shaban, Hassan

doi:10.1016/j.bspc.2021.103401

Cited by 63 publications

(24 citation statements)

References 58 publications

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“…Nature-inspired algorithms have become popular choices to solve a wide variety of optimization issues in diverse areas such as engineering [ 28 – 34 ], image processing and segmentation [ 35 – 37 ], global optimization [ 38 – 45 ], software fault prediction [ 46 ], scheduling [ 47 – 50 ], photovoltaic modeling [ 51 – 54 ], structural design problems [ 55 – 59 ], power and energy management [ 60 – 62 ], planning and routing problems [ 63 – 65 ], power take off and placements of wave energy converters [ 66 , 67 ], power consumption [ 68 , 69 ], and wind speed prediction [ 70 , 71 ]. Although the majority of nature-inspired algorithms are proposed to solve continuous problems, there have been various methods to adapt these algorithms to solve problems with discrete nature [ 72 ].…”

Section: Related Workmentioning

confidence: 99%

“…Nature-inspired algorithms have become popular choices to solve a wide variety of optimization issues in diverse areas such as engineering [28][29][30][31][32][33][34], image processing and segmentation [35][36][37], global optimization [38][39][40][41][42][43][44][45], software fault prediction [46], scheduling [47][48][49][50], photovoltaic modeling [51][52][53][54], structural design problems [55][56][57][58][59], power and energy management [60][61][62], planning and routing problems [63-65], power take off and placements of wave energy converters [66,67], power consumption [68,69], and wind speed prediction [70,71].…”

Section: Related Workmentioning

confidence: 99%

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MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

et al. 2023

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Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a considerable impact on the performance of algorithms, collaborating multiple strategies can significantly enhance the abilities of algorithms. This is our motivation to propose a multi-trial vector-based monkey king evolution algorithm named MMKE. It introduces novel best-history trial vector producer (BTVP) and random trial vector producer (RTVP) that can effectively collaborate with canonical MKE (MKE-TVP) using a multi-trial vector approach to tackle various real-world optimization problems with diverse challenges. It is expected that the proposed MMKE can improve the global search capability, strike a balance between exploration and exploitation, and prevent the original MKE algorithm from converging prematurely during the optimization process. The performance of the MMKE was assessed using CEC 2018 test functions, and the results were compared with eight metaheuristic algorithms. As a result of the experiments, it is demonstrated that the MMKE algorithm is capable of producing competitive and superior results in terms of accuracy and convergence rate in comparison to comparative algorithms. Additionally, the Friedman test was used to examine the gained experimental results statistically, proving that MMKE is significantly superior to comparative algorithms. Furthermore, four real-world engineering design problems and the optimal power flow (OPF) problem for the IEEE 30-bus system are optimized to demonstrate MMKE’s real applicability. The results showed that MMKE can effectively handle the difficulties associated with engineering problems and is able to solve single and multi-objective OPF problems with better solutions than comparative algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

et al. 2023

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“…The single diode model (SDM) and double diode model (DDM), two widely used models for solar cells, have been utilized to show the capabilities of IEO in calculating the parameters of solar cells. In another study, the authors of Reference 36 suggested a IEO based on incorporating the classical operators with the dimension learning hunting approach to handle the issue of exploration and exploitation behavior of the classical EO. Exclusively the suggested approach is tested with the multithresholding CT scan images.…”

Section: Related Workmentioning

confidence: 99%

Feature selection in high‐dimensional microarray cancer datasets using an improved equilibrium optimization approach

Balakrishnan

Dhanalakshmi

2022

Concurrency and Computation

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Optimal feature selection of a high-dimensional micro-array datasets has gained a significant importance in medical applications for early detection and prevention of disease. Traditional Optimal feature selection percolates through a population-based meta-heuristic optimization technique, a Machine Learning classifier and traditional wrapper method for transforming the original feature set into a better feature set.These techniques require a number of iterations for the convergence of random solutions to the global optimum with high-dimensionality issues such as over-fitting, memory constraints, computational costs, and low accuracy. In this article, an efficient equilibrium optimization technique is proposed for an optimized feature selection that increases the diversity of the population in the search space through Random Opposition based learning and classify the best features using a 10-fold cross-validation-based wrapper method. The proposed method is tested with six standard micro-array datasets and compared with the conventional algorithms such as Marine Predators Algorithm, Harris Hawks Optimization, Whale Optimization Algorithm, and conventional Equilibrium Optimization. From the statistical results using the standard metrics, it is interpreted that the proposed method converges to the global minimum in a few iterations through optimized feature selection, fitness value and higher classification accuracy. This proves its efficacy in exploring and finding a better solution as compared to the counterpart algorithms. In addition to complexity analysis, these results indicate a global optimum solution, an effective representation of least amount of data-high dimensionality reduction and an avoidance of over-fitting problems.

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“…Authors in [ 14 ] applied Opposition Based Learning (OBL) at the initialization phase of EO for parameters identification of photovoltaic modules. In [ 15 ], authors combined the dimension learning hunting (DLH) with EO for multi-thresholding based COVID-19 CT images. Authors in [ 16 ], the support vector regression (SVR) method with equilibrium optimizer (EO) is combined for stock market prediction.…”

Section: Introductionmentioning

confidence: 99%

Development and application of equilibrium optimizer for optimal power flow calculation of power system

et al. 2022

Self Cite

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This paper proposes an enhanced version of Equilibrium Optimizer (EO) called (EEO) for solving global optimization and the optimal power flow (OPF) problems. The proposed EEO algorithm includes a new performance reinforcement strategy with the Lévy Flight mechanism. The algorithm addresses the shortcomings of the original Equilibrium Optimizer (EO) and aims to provide better solutions (than those provided by EO) to global optimization problems, especially OPF problems. The proposed EEO efficiency was confirmed by comparing its results on the ten functions of the CEC’20 test suite, to those of other algorithms, including high-performance algorithms, i.e., CMA-ES, IMODE, AGSK and LSHADE_cnEpSin. Moreover, the statistical significance of these results was validated by the Wilcoxon’s rank-sum test. After that, the proposed EEO was applied to solve the the OPF problem. The OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus test system for different objectives. The obtained results of the proposed EEO algorithm is compared to the original EO algorithm and those obtained using other techniques mentioned in the literature. These Simulation results revealed that the proposed algorithm provides better optimized solutions than 20 published methods and results as well as the original EO algorithm. The EEO superiority was demonstrated through six different cases, that involved the minimization of different objectives: fuel cost, fuel cost with valve-point loading effect, emission, total active power losses, voltage deviation, and voltage instability. Also, the comparison results indicate that EEO algorithm can provide a robust, high-quality feasible solutions for different OPF problems.

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An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer

Cited by 63 publications

References 58 publications

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

Feature selection in high‐dimensional microarray cancer datasets using an improved equilibrium optimization approach

Development and application of equilibrium optimizer for optimal power flow calculation of power system

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