Search Results - (( solution population using algorithm ) OR ( program implementation using algorithm ))

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  1. 1

    Implementation Of Hardware Software Partitioning In Embedded System by Mohd Nor, Masyirah

    Published 2018
    “…The parameter setting is used for both algorithms is 15 number of task/node, 500 maximum number of iteration, 100 no of particles/population size and the pre-defined hardware and software area and execution time for each task as the input of the algorithms. …”
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    Monograph
  2. 2

    Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution by Mohammed, Mazin Abed, Abd Ghani, Mohd Khanapi, Hamed, Raed Ibraheem, A. Mostafa, Salama, Ahmed Ibrahim, Dheyaa, Jameel, Humam Khaled, Alallah, Ahmed Hamed

    Published 2017
    “…The structure of the algorithm is designed so that the program does not require a large database to store the population, which speeds up the implementation of the program execution to obtain the solution; secondly, the algorithm has proven its success in solving the problem and finds a shortest route. …”
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    Article
  3. 3

    Modified ant colony optimization algorithms for deterministic and stochastic inventory routing problems / Lily Wong by Lily , Wong

    Published 2018
    “…The computational results show that the algorithms which implement this new formulation are able to produce better solutions. …”
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    Thesis
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    Sub-route reversal repair mechanism and differential evolution for urban transit network design problem by Tarajo, Buba Ahmed

    Published 2017
    “…From the literature of UTNDP, the most widely used metaheuristic is the genetic algorithm, at the expense of other population-based metaheuristics. …”
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    Thesis
  6. 6

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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    Thesis
  7. 7

    Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

    Published 2019
    “…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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    Thesis
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  10. 10

    Using genetic algorithms to optimise land use suitability by Pormanafi, Saeid

    Published 2012
    “…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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    Thesis
  11. 11

    Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi M... by Mohammed, Daw Saleh Sasi

    Published 2016
    “…Contrary to the conventional approaches, which mainly rely on dispatching of each limited energy unit (LEU) in sequential order, the proposed algorithm comprising with optimization technique is used as an alternative for performing LEU dispatch; which has a tangible impact to improve and increase the AUC value. …”
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    Thesis
  12. 12

    Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi M... by Sasi Mohamme, Daw Saleh

    Published 2017
    “…Contrary to the conventional approaches, which mainly rely on dispatching of each limited energy unit (LEU) in sequential order, the proposed algorithm comprising with optimization technique is used as an alternative for performing LEU dispatch; which has a tangible impact to improve and increase the AUC value. …”
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    Book Section
  13. 13

    Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique by Ali, Amira Noor Farhanie, Sulaima, Mohamad Fani, Tahir, Musthafah Mohd., Razak, Intan Azmira Wan Abdul, Kadir, Aida Fazliana Abdul, Rahman, Zulkifli Ab.

    Published 2023
    “…The study's findings will assist manufacturers in transitioning to the ETOU tariff and contribute to the national DSM initiative program. Future research may examine other optimization algorithms and load forecasting models to refine ETOU tariff rate price reduction strategies and define available load for specific load management strategies.…”
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    Conference or Workshop Item
  14. 14

    Overview of metaheuristic: classification of population and trajectory by Zainul Rashid, Zarina

    Published 2010
    “…Algorithms are used to find the solutions through the computer program. …”
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    Monograph
  15. 15

    Multi-population Genetic Algorithm for Rich Vehicle Routing Problems by Agany Manyiel, Joseph Mabor

    Published 2020
    “…In this pa�per we present Multi-population Genetic Algorithm for Rich Vehicle Routing Prob�lems (MPGA-RVRP) to provide diversity and delay premature convergence in GA by making use of multiple populations that share potential solutions among each other and evolve independently optimising only one objective. …”
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    Final Year Project
  16. 16

    Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem by Wahid, Juliana, Mohd Hussin, Naimah

    Published 2017
    “…To produce a population of initial solution require algorithm that can produce multiple feasible solutions and these solutions must be diverse. …”
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    Conference or Workshop Item
  17. 17

    The impact of population size on knowledge acquisition in genetic algorithms paradigm: Finding solutions in the game of Sudoku by Abu Bakar, Nordin, Mahadzir, Muhammad Fadhil

    Published 2010
    “…Population size is an important component in genetic algorithms (GAs).The concept of population in GAs has contributed to a unique searching strategy which empower its search process through the massive volume of the data in a population.The purpose of this study is to see how the impact of population size on genetic algorithms in producing correct solution for a Sudoku puzzle.Sudoku is a Japanese number puzzle game that has become a worldwide phenomenon.The puzzle involves completing a grid of cells by assigning a single number to each cell.The numbers in a row or a column must consist of any one of the numbers from 1 to 9; no repetition is allowed.GA will be used to generate the correct solution of Sudoku puzzles.The mechanism to produce legal Sudoku grid will follow the requirements needed and meet all the constraints.A fitness function is designed to evaluate legal grids and GA will be tested for performance and time efficiency.The challenges lie on how GA will represent a Sudoku grid in the process and the effectiveness of its operators such as crossover and mutation.The results show how different population size can produce different solutions.The best performance is observed at 500 population size.The paper will conclude with an insight of this value and its significance to the knowledge acquisition in GA paradigm..…”
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    Conference or Workshop Item
  18. 18

    Combining approximation algorithm with genetic algorithm at the initial population for NP-complete problem by Razip, H., Zakaria, M.N.

    Published 2018
    “…In Genetic Algorithm (GA), the prevalent approach to population initialization are heuristics and randomization. …”
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    Article
  19. 19

    Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

    Published 2019
    “…Last but not least, HKA has a very high dependency on the Gaussian assumption. The proposed population-based SKF algorithm and the single solution-based SKF algorithm use the scalar model of discrete Kalman filter algorithm as the search strategy to overcome these flaws. …”
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    Thesis
  20. 20

    Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

    Published 2012
    “…Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. …”
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    Thesis