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  1. 1
    Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja

    Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja by Nurul Hani , Nortaja

    Published 2004
    “…The development process for this simulation is using programming language of Microsoft Visual C I t 6.0 and the coding creation depends on the algorithm flowchart and the formula pseudocode. …”
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  2. 2
    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms

    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…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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  3. 3
    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…From the simulation result, by using these sensors information the AUTOWiSARD algorithm can successfully differentiate and classify states without supervision, while the Q-learning algorithm is able to produce and optimized states-actions policy. …”
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  4. 4
    An integrated reservoir modelling and evolutionary algorithm for optimizing field development in a mature fractured reservoir

    An integrated reservoir modelling and evolutionary algorithm for optimizing field development in a mature fractured reservoir by Sambo, C.H., Hematpour, H., Danaei, S., Herman, M., Ghosh, D.P., Abass, A., Elraies, K.A.

    Published 2016
    “…Examining two different optimization approaches used in this work, the genetic algorithm program gave results similar to the results that were obtained by an exhaustive method with much less computation time which is a great issue mainly for large size fields or fields which possess condensate gas and require the use of compositional simulators. …”
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  5. 5
    A new meta heuristic evolutionary programming (NMEP) in optimizing economic energy dispatch

    A new meta heuristic evolutionary programming (NMEP) in optimizing economic energy dispatch by Mohamad Ridzuan, Mohamad Radzi, Hassan, Elia Erwani, Abdullah, Abdul Rahim, Bahaman, Nazrulazhar, Abdul Kadir, Aida Fazliana

    Published 2016
    “…The proposed optimization algorithm, namely New Meta-Heuristic Evolutionary Programming (NMEP) algorithm is followed to Meta-Heuristic Evolutionary Programming (Meta-EP) approach with some modification where the cloning process embedded as a significant progress during the implementation. …”
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  6. 6
    Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris

    Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris by Mohammad Aris, Ahmad Amiruddin

    Published 2019
    “…The proposed co-simulation process is developed by coupling building energy simulation (BES) software, Energy Plus with multi-objective evolutionary programming (MOEP) algorithm which is implemented in Matlab using coupling software, BCVTB. …”
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  7. 7
    Digital beamforming implementation of switch-beam smart antenna system by using integrated digital signal processor and field-programmable gate array

    Digital beamforming implementation of switch-beam smart antenna system by using integrated digital signal processor and field-programmable gate array by Vakilian, Vida

    Published 2008
    “…After modeling, the algorithm is implemented in DSP. By using the Hardware- In-Loop facility of DSP, the comparison between hardware implementation and software modeling is performed. …”
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  8. 8
    A DNR by Using Rank Evolutionary Particle Swarm Optimization for Power Loss Minimization

    A DNR by Using Rank Evolutionary Particle Swarm Optimization for Power Loss Minimization by Sulaima, Mohamad Fani, Othman, Siti Atika, Jamri , Mohd Saifuzam, Omar, Rosli, Sulaiman , Marizan

    Published 2014
    “…The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional Evolutionary Programming (EP) algorithm with a rejuvenation of the additional of ranking element. …”
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  9. 9
    Solving a multi-period inventory routing problem with stochastic unstationary demand rates

    Solving a multi-period inventory routing problem with stochastic unstationary demand rates by Muhammad Khodri Harahap, Afif Zuhri

    Published 2022
    “…A customer’s demand rates in each period are stochastic unstationary and the warehouse is implementing a VMI. This problem is solved using a simulation software called a mathematical programming language (AMPL) to achieve the optimization result. …”
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  10. 10
    Solving a multiperiod inv entory routing problem with stochastic unstationary demand rates

    Solving a multiperiod inv entory routing problem with stochastic unstationary demand rates by Muhammad Khodri Harahap, Afif Zuhri

    Published 2022
    “…A customer’ s demand rates in each period are stochastic unstationary and the warehouse is implementing a VMI. This problem is solved using a simulation software called a mathematical programming language (AMPL) to achieve the optimization result. …”
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  11. 11
    Simulated Kalman Filter algorithms for solving optimization problems

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

    Published 2019
    “…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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  12. 12
    Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems

    Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems by Tan, Joe Yee

    Published 2022
    “…In this case, the effectiveness and reliability of RMS is increase by combining the simulation with the optimization algorithm.…”
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  13. 13
    A discrete simulated kalman filter optimizer for combinatorial optimization problems

    A discrete simulated kalman filter optimizer for combinatorial optimization problems by Suhazri Amrin, Rahmad

    Published 2022
    “…However, these extensions may result in increased execution times for the algorithm. In this research, a new combinatorial algorithm named discrete simulated Kalman filter optimizer (DSKFO) is proposed to solve combinatorial optimization problem. …”
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  14. 14
    Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm

    Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm by Zuwairie, Ibrahim, Nor Hidayati, Abd Aziz, Nor Azlina, Ab. Aziz, Saifudin, Razali, Mohd Saberi, Mohamad

    Published 2016
    “…In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. …”
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  15. 15
    Ant colony optimization for rule induction with simulated annealing for terms selection

    Ant colony optimization for rule induction with simulated annealing for terms selection by Saian, Rizauddin, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
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  16. 16
    Optimization of turning parameters using genetic algorithm method

    Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

    Published 2008
    “…The simulation based on Genetic Algorithm are successful develop and the optimum parameters values are obtained from the simulation.…”
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  17. 17
    The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation

    The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation by Hamizan, Sharbini, Roselina, Sallehuddin, Habibollah, Haron

    Published 2023
    “…This paper proposes a new hybrid of nature inspired optimization algorithm (IFDO-WOABAT) based on the latest optimization algorithm namely Improved Fitness Dependent Optimization (IFDO) with Whale-Bat Optimization algorithm (WOABAT). …”
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    Proceeding
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  18. 18
    Asynchronous simulated kalman filter optimization algorithm

    Asynchronous simulated kalman filter optimization algorithm by Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Nor Hidayati, Abd Aziz, Tasiransurini, Ab Rahman

    Published 2018
    “…Simulated Kalman filter (SKF) is an optimization algorithm which is inspired by Kalman filtering method. …”
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  19. 19
    Simulated Kalman Filter optimization algorithm for maximization of wireless sensor networks coverage

    Simulated Kalman Filter optimization algorithm for maximization of wireless sensor networks coverage by Nor Azlina, Ab. Aziz, Zuwairie, Ibrahim, Kamarulzaman, Ab Aziz, Nor Hidayati, Abdul Aziz

    Published 2019
    “…Simulated Kalman Filter (SKF) is a population based optimization algorithm inspired by the Kalman filtering method. …”
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  20. 20
    Predictive-reactive job shop scheduling for flexible production systems with the combination of optimization and simulation based algorithm

    Predictive-reactive job shop scheduling for flexible production systems with the combination of optimization and simulation based algorithm by Abdul Rahman, Azrul Azwan, Joe Yee, Tan, A Rahman, Muhamad Arfauz, Salleh, Mohd Rizal, Bilge, Pinar

    Published 2020
    “…The predictive part determines the feasible schedule to be used for a flow shop which is generated using a combination of rule-based simulation and optimization: first, using the optimization algorithm to compute a rough plan, followed by using a rule based simulation system to locally fine tune the plan to obtain the final schedule. …”
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