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Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation
Published 2018“…This algorithm is invariably called the modified greedy algorithm (MGA). …”
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A variable combinatorial test suite strategy based on modified greedy algorithm
Published 2015“…This proposed will suggest a new CIT strategy based on a modified greedy algorithm (MGA) with addressing the supporting of variable strength interaction to generate a satisfactory test suite size.…”
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Optimized clustering with modified K-means algorithm
Published 2021“…In order to obtain the optimum number of clusters and at the same time could deal with correlated variables in huge data, modified k-means algorithm was proposed. …”
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An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion
Published 2018“…Performance of each algorithm is compared in terms of number of test cases generated as well as the run time required. …”
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Minimizing power loss using modified artificial bee colony algorithm / Nur Azlin Ashiqin Mohd Amin ...[et al.]
Published 2021“…These control variable values are adjusted for loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system. …”
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A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. …”
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Using adaptive safe experimentation dynamics algorithm for maximizing wind farm power
Published 2022“…The ASEDA method is an improved version of the Safe Experimentation Dynamics (SED) algorithm that modifies the current tuning variable to respond to the changes in the objective function. …”
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Using adaptive safe experimentation dynamics algorithm for maximizing wind farm power production
Published 2022“…The ASEDA method is an improved version of the Safe Experimentation Dynamics (SED) algorithm that modifies the current tuning variable to respond to the changes in the objective function. …”
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An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation
Published 2023“…The iSSA is applied to OPF problems involving stochastic solar power generation, with the goal of optimizing control variables such as real power generation, voltage magnitude at generation buses, transformer tap settings, and reactive power compensation. …”
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Optimum Feeder Routing and Distribution Substation Placement and Sizing using PSO and MST
Published 2014“…This paper proposes an algorithm to find the optimum distribution substation placement and sizing by utilizing the PSO algorithm and optimum feeder routing using modified MST. …”
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An ensemble of neural network and modified grey wolf optimizer for stock prediction
Published 2019“…Additionally, the research restricts the number of variables through feature selection to enhance the performance of the algorithm. …”
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An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Enhanced Intelligent Water Drops Algorithm for University Examination Timetabling Problems
Published 2024“…(iv) In addition, IWD has two variables that have a large effect on the performance and the convergence of the algorithm (Alijla et al., 2013). …”
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Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment
Published 2017“…The proposed strategy is dependent on modified Zimmermanns approach for handling all inexact operating costs, data capacities, and demand variables. …”
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Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak
Published 2025“…The model employs a two-level structure, where the first level uses standard soft constraints as the objective function to evaluate solution quality, while the second level dynamically adapts to faculty-specific preferences. A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
Published 2018“…To deal with these problems, this thesis introduces three approved intelligent controllers for hydropower generation. Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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