An Enhanced Evaporation Rate Water-Cycle Algorithm for Global Optimization

Hussien, Abdelazim G.; Hashim, Fatma A.; Qaddoura, Raneem; Abualigah, Laith; Pop, Adrian

doi:10.3390/pr10112254

Cited by 24 publications

(4 citation statements)

References 64 publications

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“…In the future, Authors can use many optimization algorithms and embedded them in the network for better accuracy. These algorithms can be any algorithm such as Snake Optimizer (SO) 45 , Fick’s Law Algorithm (FLA) 46 , Jellyfish Search (JS) 47 , Dandelion Optimizer (DO) 48 , Aquila Optimizer 49 – 51 , Atom Search Optimization (ASO) 52 , Water Cycle Algorithm (WCA) 53 , Bald Eagle Search (BES) 54 , African Vultures Optimization Algorithm (AVOA) 55 , Archimedes Optimization Algorithm (AOA) 56 , Beluga Whale Optimization (BWO) 57 , Hunter Prey Optimization (HPO) 58 , INFO 59 , Supply Demand Optimizer 60 , 61 , Reptile Search Algorithm (RSA) 62 , Golden Jackle Optimization (GJO) 63 , and more.…”

Section: Discussionmentioning

confidence: 99%

A CNN-based model to count the leaves of rosette plants (LC-Net)

Deb,

Dhal,

Das

et al. 2024

Sci Rep

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Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

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Section: Discussionmentioning

confidence: 99%

A CNN-based model to count the leaves of rosette plants (LC-Net)

Deb,

Dhal,

Das

et al. 2024

Sci Rep

Self Cite

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“…Meta-heuristic approaches have gained widespread popularity as effective strategies for solving real-world problems and finding optimal solutions. Some of the well-known metaheuristic algorithms include the genetic algorithm (GA) and its variants [3][4][5][6][7], Sine Cosine Algorithm (SCA) [8], the Orchard algorithm (OA) [9], Snake Optimizer (SO) [10], Jellyfish Optimizer (JS) [11], Fick's Law Algorithm (FLA) [12], the successive history-based adaptive differential evolutionary (SHADE) [13] algorithm, the enhanced chaotic grasshopper optimization algorithm (ECGOA) [14], the Aphid-Ant Mutualism (AAM) [15], pattern search algorithms (PSA) and their variants [16][17][18][19][20][21][22], among others. In the field of power systems, the optimal power flow (OPF) problem using thermal power generators has been extensively studied, with several meta-heuristic algorithms being applied.…”

Section: Figurementioning

confidence: 99%

An enhanced hunter‐prey optimization for optimal power flow with FACTS devices and wind power integration

Hassan

Daqaq

Kamel

et al. 2023

IET Generation Trans & Dist

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This paper proposes an improved version of the Hunter‐prey optimization (HPO) method to enhance its search capabilities for solving the Optimal Power Flow (OPF) problem, which includes FACTS devices and wind power energy integration. The new algorithm is inspired by the behavior of predator and prey animals, such as lions, wolves, leopards, stags, and gazelles. The primary contribution of this study is to address the tendency of the original HPO approach to get trapped in local optima, by proposing an enhanced Hunter‐prey optimization (EHPO) approach that improves both the exploration and exploitation phases. This is achieved through a random mutation for exploration and an adaptive process for exploitation, which balances the transition between the two phases. The performance of the EHPO algorithm is compared with other optimization algorithms, and subsequently, it is used to solve the OPF problem incorporating FACTS devices and wind power. The results demonstrate the effectiveness and superiority of the proposed algorithm. In conclusion, this study successfully enhances the EHPO algorithm to provide better accuracy and faster convergence in finding optimal solutions for complex real‐world problems.

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“…The method is applied to solve benchmark as well as real-world problems and showed better efficiency than the basic version. Embedding local escape operator in updating process of evaporation rate water cycle algorithm, another improved version is suggested and showed adequate performance in solving global optimization problems [62]. An optimization technique based on simple mathematical operations such as arithmetic optimization algorithm has shown satisfactory performance on image fusion at feature level [63].…”

Section: Related Studiesmentioning

confidence: 99%

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

2022

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Chemical reaction optimization (CRO) algorithm is a robust metaheuristic by simulating the process of natural chemical reaction and has been applied for solving numerous realistic problems. However, it is suffering from low population diversity, slow convergence, and deprived from local search ability. To boost the search operation of CRO, this article proposes an improved CRO method termed as ICRO. We achieve the improvements in two phases. First, to start the search operation with a better-quality population, we propose a new method to initialize population which helps in enhancing diversification of the search space. Second, to intensify the local search ability we integrate Nelder-Mead simplex method with CRO. The ICRO is then evaluated on a few benchmark functions for optimization and found apposite. Dendritic neuron model (DNM) using additive and multiplicative-based aggregation functions has been emerging as a machine learning approach and found successful in many engineering applications. This study attempts to advance the predictive accuracy of DNM through maintaining a decent steadiness between exploitation and exploration of its search space with the proposed ICRO, termed as ICRO-DNM. The powerful global search ability of ICRO synergies with better approximation capability of DNM thus, able to overcome the limitations of conventional back propagation learning based DNM. ICRO-DNM is evaluated on closing price prediction for four stock index and net asset values of four mutual fund datasets and found efficient. The consequences of this attempt are: (1) Proposed ICRO shows robust parameter optimization ability for benchmark functions and DNM compared to basic CRO, particle swarm optimization (PSO), and genetic algorithm (GA); (2) The learning paradigm formed due to reasonable amalgamation of ICRO and DNM is pretty able to capture the underlying uncertainties coupled with financial data, produces more precise and steady predictions and significantly different from other forecasts.

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An Enhanced Evaporation Rate Water-Cycle Algorithm for Global Optimization

Cited by 24 publications

References 64 publications

A CNN-based model to count the leaves of rosette plants (LC-Net)

A CNN-based model to count the leaves of rosette plants (LC-Net)

An enhanced hunter‐prey optimization for optimal power flow with FACTS devices and wind power integration

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

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