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Optimizing the placement of fire department in Kulim using greedy heuristic and simplex method / Muhammad Abu Syah Mohd Suzaly
Published 2023“…Greedy heuristics is a type of optimization algorithm that makes decisions based on locally optimal solutions. …”
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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio...
Published 2023“…Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. …”
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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction
Published 2023“…This paper proposes an optimization algorithm based on the Binary Bat algorithm methodology for replacing the conventional orthogonal array approach. …”
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Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
Published 2025“…This paper formulates a mixed integer non-linear probabilistic optimization planning problem to determine the optimal location, power rating and capacity of compressed air energy storage system (CAES) for a hybrid power system that includes wind and photo-voltaic (PV) energy sources. …”
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Modification of particle swarm optimization algorithm for optimization of discrete values
Published 2011“…Stochastic-type algorithms have shown good performance in locating the global optima in optimization problems, even in difficult search spaces [1). …”
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A variable-capacity-based fuzzy random facility location problem with VaR objective
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Solving the integrated inventory supply chain problems using meta-heuristic methods / Seyed Mohsen Mousavi
Published 2018“…A Modified Particle Swarm Optimization (MPSO) algorithm, a Genetic Algorithm (GA), a modified fruit fly optimization algorithm (MFOA) and a simulated annealing (SA) algorithm were used to find the optimal solution. …”
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Optimum MV Feeder Routing and Substation siting and rating in Distribution Network
Published 2014“…This paper proposes an evolutionary algorithm to determine the optimum distribution substation placement and sizing by using the particle swarm optimization algorithm and optimum feeder routing using modified minimum spanning tree algorithm. …”
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Optimal timber transportation planning in tropical hill forest using bees algorithm
Published 2022“…This study proposed a multi-objective linear programming model with Bees algorithm (BA) to find an optimal cost TTP for extraction, forest road, and landing locations. …”
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Metaheuristic Algorithm for Wellbore Trajectory Optimization
Published 2019“…A variety of possible well types and so many complex drilling variables and constraints make the wellbore optimization problem a very challenging work. …”
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Power quality improvement in distribution systems considering Optimum D-STATCOM placement
Published 2013“…The obtained results are compared with the particle swarm optimization and the genetic algorithm. The simulation and comparison results verify the ability of the FA to accurately determine the optimal location and size of the D-STATCOM in radial distribution systems.…”
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Optimizing timber transportation planning for timber harvesting using bees algorithm in Malaysia
Published 2023“…Two types of extraction machines with unique characteristics to be applied in timber harvest area in Malaysia is combined for timber harvesting with the aim each machine will extract timber suitable to their ability. A Bees Algorithm (BA) was proposed to find an optimum TTP for timber extraction, forest road, and landing locations with grid cell-sized 10 m × 10 m and attributed with fixed and variable costs. …”
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Optimal placement of TCSC for reactive power planning using grasshopper optimization algorithm considering line outage (N-M)
Published 2019“…Standard IEEE-30 bus test system has been applied to the test system. Optimal setting of all control variables, namely locations and the sizes of the TCSC has been determined by GOA and power flow analysis method. …”
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Global Algorithms for Nonlinear Discrete Optimization and Discrete-Valued Optimal Control Problems
Published 2009“…Furthermore, variable switching times are known to cause problems in the implementation of any numerical algorithm due to the variable location of discontinuities in the dynamics. …”
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Acoustic emission partial discharge localization in oil based on artificial bee colony
Published 2025“…Comparisons with the genetic algorithm (GA), particle swarm optimization (PSO) and bat algorithm (BA) revealed that the distance error, maximum deviation and computation time for AE PD localization based on ABC are the lowest. …”
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Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System
Published 2018“…The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. …”
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Maximum Power Point Tracking (MPPT) Based Particle Swarm Optimization (PSO) for hydrokinetic energy harnessing
Published 2024“…This paper presents a design and modeling of the Particle Swarm Optimization (PSO)-based maximum power point tracking (MPPT) algorithm specifically tailored for variable-speed fixed-pitch vertical axis hydrokinetic turbines. …”
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Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania
Published 2025“…We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). …”
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