Search Results - (( java simulation optimization algorithm ) OR ( using generating optimisation algorithm ))
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Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions
Published 2013“…The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. …”
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Optimization of multi-holes drilling path using particle swarm optimization
Published 2022“…The performance of PSO was then compared with other meta-heuristic algorithms, including Genetic Algorithm (GA) and Ant Colony Optimisation (ACO), Whale Optimisation Algorithm (WOA), Ant Lion Optimiser (ALO), Dragonfly Algorithm (DA), Grasshopper Optimisation Algorithm (GOA), Moth Flame Optimisation (MFO) and Sine Cosine Algorithm (SCA). …”
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Optimisation of automatic generation control performance in two-area power system with pid controllers using mepso / Lu Li
Published 2018“…The AGC in two-area power system was constructed and simulated by using MATLAB R2017b software. From the simulation results, it was found that with the same number of PID controllers, the performance of AGC optimised by using MEPSO-TVAC algorithm is better in terms of overshoot and fitness value than using EPSO and PSO algorithms. …”
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4
Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm
Published 2019“…These models were used to find the optimised the network output using genetic algorithm search. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…The simulation is implemented with iFogSim and java programming language. …”
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Modelling of optimal placement and sizing of battery energy storage system using hybrid whale optimization algorithm and artificial immune system for total system losses reduct...
Published 2023“…Having those said, this study proposes BESS optimisation to reduce the total system losses using modified Whale Optimisation Algorithm (WOA) with high exploration and exploitation features. …”
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Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail
Published 2024“…In this study, a new optimisation algorithm termed Hybrid Evolutionary-Barnacles Mating Optimisation (HEBMO) was initially formulated to solve optimisation problems. …”
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The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
Published 2020“…Technically, BFOA has been applied as supplementary algorithm for optimizing weight, parameters for other classifier algorithms and selecting optimised features for other classifiers. …”
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Optimised Crossover Genetic Algorithms for Combinatorial Optimisation Problems
Published 2010“…A Genetic Algorithm is successful in generating near -optimal solutions if it is able to produce o®spring during crossover that is better than the parent solutions. …”
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10
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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11
Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
Published 2013“…The aim of this research is to establish a methodology and algorithm for integrating ASP and ALB optimisation using Particle Swarm Optimisation. …”
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12
Development of a Tuneable Test Problem Generator for Assembly Sequence Planning and Assembly Line Balancing
Published 2012“…Experiments confirm that the selected combination of input attributes does control the generated assembly sequence planning and assembly line balancing problem complexity, and also that the generated problems can be used to identify the suitability of a given algorithm to problem types.…”
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Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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15
Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production
Published 2013“…The trained networks model predictions were used to generate a maximum biogas output assisted by genetic algorithms optimisation. …”
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Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of blogas production
Published 2013“…This paper proposed a framework to model and optimises a biogas production using artificial neural networks and genetic algorithms.The intelligence computation was applied to achieve a better model and optimisation compared to a mathematical modeling.Two training approaches were used to train a set of neural networks design. …”
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Multi-objective optimisation of assembly line balancing type-e problem with resource constraints
Published 2016“…Later, the optimisation algorithm will be tested and verified using test problems from literature. …”
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Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms
Published 2021“…Among the hybrid models, in terms of accuracy, the best optimisation algorithm at station 1K06 was the AMFO while the best optimisation algorithm at station 1K07 was the HPSOGA. …”
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Analyzing The Potential Of Genetic Algorithm For Maximum Power Point Tracking In Wind Energy Conversion System In Malaysia
Published 2023“…In this paper, a maximum power point tracking for the wind turbine is proposed which is the indirect speed control. A genetic algorithm is used to further optimised the control strategy by finding the optimised variable for the controller. …”
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Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production
Published 2013“…The trained networks model predictions were used to generate a maximum biogas output assisted by genetic algorithms optimisation. …”
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