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  1. 1

    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA) by Mohiuddin, A. K. M., Ashour, Ahmed Aly Ibrahim Shaaban, Yap, Haw Shin

    Published 2008
    “…This paper aims to demonstrate the effectiveness of Multi- Objective Genetic Algorithm Optimization and its robust practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. …”
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    Proceeding Paper
  2. 2

    Application of Multi-objective Genetic Algorithm (MOGA) for design optimization of valve timing at various engine speeds by Mohiuddin, A. K. M., Rahman, Mohammed Ataur, Haw Shin, Yap

    Published 2011
    “…This paper aims to demonstrate the effectiveness of Multi-Objective Genetic Algorithm Optimization and its practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. …”
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    Article
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    Evolution of RF-signal cognition for wheeled mobile robots using pareto multi-objective optimization by Chin, Kim On, Teo, Jason Tze Wi

    Published 2009
    “…A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. …”
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    Article
  5. 5

    Evolutionary multi-objective optimization of autonomous mobile robots in neural-based cognition for behavioural robustness by Chin, Kim On, Teo, Jason Tze Wi, Azali Saudi

    Published 2009
    “…Furthermore, the controllers’ moving performances, tracking ability and robustness also have been demonstrated and tested with four different levels of environments. The experimentation results showed the controllers allowed the robots to navigate successfully, hence demonstrating the EMO algorithm can be practically used to automatically generate controllers for phototaxis and RF-localization behaviors, respectively. …”
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    Chapter In Book
  6. 6

    Benchmark simulator with dynamic environment for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana

    Published 2014
    “…Characteristics of jobs and resources to be used in evaluating the performance of the scheduling algorithm must reflect the dynamic nature of real grid environment.Static models of jobs and resources cannot be used to generate jobs and resources in simulating the grid environment because of the dynamic nature of the grid.This paper presents a new graph representation of jobs and resources which is practical for hybrid metaheuristic model implementation such as ant colony optimization and genetic algorithm.A dynamic model that can generate jobs and resources similar to the jobs and resources in the real grid environment is also proposed.Jobs and resources may join in or drop out from the grid.Stochastic analysis is performed on the characteristics of jobs and resources.A simulator based on the dynamic expected time to compute, has been developed and can be used as a benchmark.The simulator can generate jobs and resources with the characteristics of jobs and resources in the real grid environment.This will facilitates the evaluation of dynamic job scheduling algorithm.…”
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    Conference or Workshop Item
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    Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots by Hanafi Ahmad Hijazi, Patricia Anthony

    Published 2006
    “…As a result, this study has thus shown that the multi-objective approach to evolutionary robotics in the form of the elitist PDE-EMO algorithm can be practically used to automatically generate controllers for RF-Iocalization behavior in autonomous mobile robots.…”
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    Research Report
  10. 10

    Enhanced multi-objective evolutionary mating algorithm with improved crowding distance and levy flight for optimizing comfort index and energy consumption in smart buildings by Muhammad Naim, Nordin, Mohd Herwan, Sulaiman, Nor Farizan, Zakaria, Zuriani, Mustaffa

    Published 2025
    “…These enhancements enable MOEMA to effectively navigate complex multi-objective landscapes, leading to more diverse and well-converged Pareto-optimal solutions. The algorithm's performance is thoroughly assessed using the chosen benchmark functions and validated through practical applications in smart building environments. …”
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    Article
  11. 11

    Optimization Algorithms: A Comparison Study for Scheduling Problem at UIN Raden Fatah's Sharia and Law Faculty by Mustakim, ., Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Izman, Herdiansyah

    Published 2024
    “…We focus on two prominent optimization techniques, Genetic Algorithms (GA) and Ant Colony Optimization (ACO), chosen for their capability to address the complex optimization problems typical in academic settings. …”
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    Article
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    African buffalo optimization algorithm based t-way test suite generation strategy for electronic-payment transactions by B. Odili, Julius, B. Nasser, Abdullah, A., Noraziah, Abd Wahab, Mohd Helmy, Ahmed, Mashuk

    Published 2022
    “…In this paper, therefore, a hybrid variant of the African Buffalo Optimization (ABO) algorithm is proposed for CIT. Four hybrid variants of the ABO are proposed through a deliberate improvement of the ABO with four algo�rithmic components. …”
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    Conference or Workshop Item
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    Reinforcement learning in risk management for pharmaceutical construction projects: frontiers, challenges, and improvement strategies by Junjia, Yin, Jiawen, Liu, Alias, Aidi Hizami, Haron, Nuzul Azam, Abu Bakar, Nabilah

    Published 2025
    “…Therefore, this paper reviews the practical applications of six algorithms—Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximity Policy Optimization (PPO)—in construction safety, temperature control, resource scheduling, and automated equipment optimization, validating the potential of reinforcement learning to effectively manage dynamic risks through adaptive learning. …”
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    Article
  17. 17

    Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game by Chang, Kee Tong

    Published 2015
    “…The second experiment result shows both GA and DE algorithms can generate optimal solutions with very high fitness scores but the cost of spawning was extremely high. …”
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    Thesis
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    Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

    Published 2024
    “…To address this issue, this study incorporated joint graph learning from the gmc algorithm into swmcan, creating a new algorithm called swmcan-jg. …”
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    Thesis
  19. 19

    A generalized laser simulator algorithm for mobile robot path planning with obstacle avoidance by Muhammad, Aisha, Ali, Mohammed A.H., Turaev, Sherzod, Abdulghafor, Rawad Abdulkhaleq Abdulmolla, Shanono, Ibrahim Haruna, Alzaid, Zaid, Alruban, Abdulrahman, Alabdan, Rana, Dutta, Ashit Kumar, Almotairi, Sultan

    Published 2022
    “…Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. …”
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    Article
  20. 20

    Artificial Neural Controller Synthesis in Autonomous Mobile Cognition by Kim On Chin, Jason Teo

    Published 2009
    “…The Pareto-frontier Differential Evolution (PDE) algorithm is utilized to generate the Pareto optimal sets through a 3-layer feed-forward artificial neural network that optimize the conflicting objectives of robot behavior and network complexity, where the two different types of robot behaviors are phototaxis and RF-localization, respectively. …”
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    Article