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Showing 1 - 16 results of 16 for search 'water ((pollination algorithm) OR (optimization algorithm))', query time: 0.08s Refine Results
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    Salp swarm algorithm: survey, analysis, and new applications by Abualigah, Laith

    Published 2024
    “…The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. …”
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    Performance analysis and optimization of photovoltaic-thermal system with direct contact membrane distillation using metaheuristic algorithms by Faisal Maqbool (14561804)

    Published 2025
    “…The objective is to maximize electrical efficiency, permeate flux, and outlet temperature by considering the effects of flow rates (1–10 L/min), solar irradiation (10–1000 W/m2), and ambient temperatures (291–324 K). Metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are employed to determine the optimal operating parameters. …”
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    A Literature Review on System Dynamics Modeling for Sustainable Management of Water Supply and Demand by Khawar Naeem (17984062)

    Published 2023
    “…The models included agent-based modeling (ABM), Bayesian networking (BN), analytical hierarchy approach (AHP), and simulation optimization multi-objective optimization (MOO). The solution approaches included the genetic algorithm (GA), particle swarm optimization (PSO), and the non-dominated sorting genetic algorithm (NSGA-II). …”
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    Optimization of PV Cleaning Practices: Comparison Between Performance-Based and Periodic-Based Approaches by Abdulla Alhajeri, Rashed

    Published 2020
    “…It is important to choose the right cleaning strategy (method and frequency) to maximize the electricity production and economic performance of the PV facility. An optimization algorithm was developed and tested for multiple PV panel configurations based in Dubai Water and Electricity Authority’s (DEWA) outdoor test facil ity (OTF) solar lab. …”
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    Multi-Objective Optimization for Food Availability under Economic and Environmental Risk Constraints by Bashar Hassna (19170940)

    Published 2024
    “…Deploying the Analytical Hierarchy Process (AHP), this study evaluates climate change risks associated with seven different suppliers for three key crops, considering a range of factors, including surface temperature, arable land, water stress, and adaptation policies. Utilizing these assessments, a multi-objective optimization model is developed and solved using MATLAB (R2018a)’s Genetic Algorithm, aiming to identify optimal suppliers to meet Qatar’s food demand, with consideration of the economic, environmental, and risk factors. …”
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    Seasonal variations in feed-water chemistry and fouling dynamics of reverse-osmosis systems: A global climate lens by Nasser Zareei (22921226)

    Published 2025
    “…<p>Reverse osmosis desalination plants are built for worst-case conditions, yet seasonal variations in feed water quality often outpace their design assumptions, leading to avoidable membrane performance losses. …”
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    A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns by Md Ferdous Wahid (13485799)

    Published 2023
    “…The important feature subset is identified using the modified Binary Grey Wolf Optimization Particle Swarm Optimization (BGWOPSO) algorithm. …”
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    An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring by Nitin Goyal (7876730)

    Published 2022
    “…The location prediction of mineral reservoirs like oil, gas, or metals in deep water is a challenge during the extraction of these resources. …”
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    Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces by Uzair Sajjad (19646296)

    Published 2021
    “…In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. …”
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