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Optimal damper location for mid-high frequency vibration control on built-up structures: case study using VA one
Published 2012“…Optimization is carried out using genetic algorithm. …”
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Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia
Published 2024“…Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. …”
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Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems
Published 2014“…The objectives of this research are to develop an algorithm for integrated scheduling and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm, ensure the algorithm validity and improvement on the performance of the developed algorithm. …”
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Application of sampling-based motion planning algorithms in autonomous vehicle navigation
Published 2016“…One of the challenging research areas in autonomous vehicle is the development of an intelligent motion planner, which is able to guide the vehicle in dynamic changing environments. …”
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Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
Published 2009“…While the scheduling of AGVs and machines are highly related; simultaneous scheduling of machines and AGVs has been proposed in the literature. Genetic algorithm (GA) proposed as a robust tool for optimization of scheduling problems. …”
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A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems
Published 2018“…This paper attempts the suitability of newly developed hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviated as SP-QPSO, extended specifically designed for solving constrained optimization problems. …”
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Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)
Published 2019“…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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Intelligent energy systems using the barnacles mating optimizer and evolutionary mating algorithm: Foundations, methods, and applications
Published 2026“…Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications reveals the potential of innovative optimization algorithms to support sustainability in modern energy systems. …”
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Simulation of identifying shortest path walkway in library by using ant colony optimization
Published 2012“…Therefore,by determining the shortest path will help in reducing the time consume problem.This project is developed by starting with designing the workflow diagram as well as the design of the output interface.The work flow is the guide for the process of development.In between,Heuristic Approach is used to determine the entire possible paths at first,then Ant Colony Optimization algorithm will be implemented to search for the final and the shortest path. …”
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Undergraduates Project Papers -
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A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS
Published 2017“…A fuzzy hybrid GA-PSO (genetic algorithm – particle swarm optimization) algorithm was developed to optimize the model. …”
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Development of multi-objective optimization methods for integrated scheduling of handling equipment (AGVs, QCs, SP-AS/RS) in automated container terminals
Published 2012“…Therefore, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm, were developed to optimize the integrated scheduling of handling equipment. …”
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Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms
Published 2008“…The knowledge acquired by the process is interpreted and mapped into vectors, which are kept in the database and used by the system to guide its reasoning process. Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Published 2020“…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization
Published 2017“…In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. …”
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Estimation-based Metaheuristics: A New Branch of Computational Intelligence
Published 2016“…Besides biology, physics and chemistry, state estimation algorithm also has become a source of inspiration for developing metaheuristic algorithms. …”
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Automated guided vehicle (AGV) using 68HC11 microcontroller
Published 2006“…Considering that, AGVs should be well developed to optimize it’s benefits to our own living. …”
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Undergraduates Project Papers -
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Predictive Analytics in Genetic Engineering as an Optimization Problem
Published 2024“…In genetic engineering, developing a breed with a desired trait is a search and optimization problem that sometimes requires many generations of field and laboratory experiments for an optimal solution to be found. …”
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MOSDA: A proposal for multiple objective spiral dynamics algorithm
Published 2018“…A method called “archive method” that is used in multi-objective particle swarm optimization (MOPSO) is adopted into SDA to develop its multiobjective (MO) type algorithm. …”
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