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Comparison Between Linear Programming And Integer Linear Programming: A Review
Published 2018“…This research discusses comparison of linear programming (LP) and integer linear programming (ILP). …”
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Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan
Published 2024“…This project explores the optimization of surveillance camera placements using Particle Swarm Optimization (PSO) and Mixed-Integer Linear Programming (MILP). …”
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Quality of service in mobile IP networks with parametric multi-channel routing algorithms based on linear programming approach
Published 2018“…This approach tunes the parameters of the linear programming models that are used in the other algorithms by using a dynamic element. …”
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Linear array beampattern gain optimization techniques
Published 2023“…The work describes here uses optimization techniques to increase the gain of a uniform linear array's beampattern when some of its elements fails. …”
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Path planning algorithm for a car like robot based on MILP method
Published 2013“…The final phases are the design and build coalitions of linear programs and binary constraints to avoid collision with obstacles by Integer Mixed Linear Program (MILP). …”
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Solving Sudoku puzzles in Binary Integer Linear Programming using Branch and Bound algorithm / Ahida Waliyyah Ahmad Fuad
Published 2024“…This study explores the application of a Binary Integer Linear Programming (BILP) model combined with the Branch and Bound (B&B) algorithm to solve Sudoku puzzles. …”
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A multi-depot vehicle routing problem with stochastic road capacity and reduced two-stage stochastic integer linear programming models for rollout algorithm
Published 2021“…A matheuristic approach based on a reduced two-stage Stochastic Integer Linear Programming (SILP) model is presented. The proposed approach is suitable for obtaining a policy constructed dynamically on the go during the rollout algorithm. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. …”
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Model structure selection for a discrete-time non-linear system using genetic algorithm
Published 2004“…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. …”
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Analytical Approach for Linear Programming Using Barrier and Penalty Function Methods
Published 2003“…This raises a question why are we not using this to solve the linear programming problems. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed...
Published 2022“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance
Published 2021“…Linear programming (LP) is a mathematical modelling that formulate a problem into three components which are decision variables, objective function and constraints. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed...
Published 2022“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. …”
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Charge-coupled device based on optical tomography system for monitoring two-phase flow
Published 2024Article
