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  1. 1
    Optimization of mycelium growth using genetic algorithm for multi-objective functions

    Optimization of mycelium growth using genetic algorithm for multi-objective functions by Muhamad Faiz, Abu Bakar

    Published 2019
    “…Mathematical optimization was typical use for such problem, in which it was supposed to maximizing or minimizing a function. …”
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    Undergraduates Project Papers
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  2. 2
    Fuzzy modeling using Bat Algorithm optimization for classification

    Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

    Published 2018
    “…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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  3. 3
    Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid

    Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid by Ku-Mahamud, Ku Ruhana, Ramli, Razamin, Yusof, Yuhanis, Mohamed Din, Aniza, Mahmuddin, Massudi

    Published 2012
    “…Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. …”
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    Monograph
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  4. 4
    Comparative Performance Analysis of Bat Algorithm and Bacterial Foraging Optimization Algorithm using Standard Benchmark Functions

    Comparative Performance Analysis of Bat Algorithm and Bacterial Foraging Optimization Algorithm using Standard Benchmark Functions by Mazlina, Abdul Majid, Alsariera, Yazan A., Alamri, Hammoudeh S., Nasser, Abdullah M., Kamal Z., Zamli

    Published 2014
    “…Owing to the fact that they are new and much of their relative performance are still unknown (as compared to other established meta-heuristic algorithms), Bacterial Foraging Optimization Algorithm (BFO) and Bat Algorithm (BA) have been adopted for comparison using the 12 selected benchmark functions. …”
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  5. 5
    Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

    Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints by M.M., Razali, M. F. F., Ab Rashid, M. R. A., Make

    Published 2020
    “…Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem whichrequires an effective algorithm for solution. …”
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  6. 6
    African Buffalo Optimization

    African Buffalo Optimization by Odili, Julius Beneoluchi, M. N. M., Kahar

    Published 2016
    “…This paper presents an overview of major metaheuristic algorithms with the aim of providing a basis for the development of the African Buffalo Optimization algorithm which is a nature-inspired, population-based metaheuristic algorithm. …”
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  7. 7
    A carnivorous plant algorithm for solving global optimization problems

    A carnivorous plant algorithm for solving global optimization problems by Ong, Kok Meng, Ong, Pauline, Sia, Chee Kiong

    Published 2021
    “…In this study, a novel metaheuristic algorithm, namely, carnivorous plant algorithm (CPA), inspired by how the carnivorous plants adapting to survive in the harsh environment, was proposed. …”
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  8. 8
    An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion

    An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion by Haque, Ariful

    Published 2018
    “…We have chosen four neighborhood based algorithms which are commonly used in optimization problems and divided them in newly implemented and re-implemented category. …”
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  9. 9
    Hybrid harmony search algorithm for continuous optimization problems

    Hybrid harmony search algorithm for continuous optimization problems by Ala’a Atallah, Hamad Alomoush

    Published 2020
    “…Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). …”
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  10. 10
    Comparative implementation of the benchmark Dejong 5 function using flower pollination algorithm and the African buffalo optimization

    Comparative implementation of the benchmark Dejong 5 function using flower pollination algorithm and the African buffalo optimization by Odili, Julius Beneoluchi, Noraziah, Ahmad

    Published 2019
    “…In addition, it explores the Dejong 5 function with the hope of attracting the attention of the research community to evaluate the capacity of the two comparative algorithms as well as the Dejong 5 function. …”
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  11. 11
    Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning

    Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning by Alomoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Aloufi, Khalid, Kamal Z., Zamli

    Published 2019
    “…GWO-HS was evaluated using 24 classical benchmark functions with 30 state-of-the-art benchmark functions from CEC2014. …”
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  12. 12
    A Comparison of Particle Swarm optimization and Global African Buffalo Optimization

    A Comparison of Particle Swarm optimization and Global African Buffalo Optimization by Adam Kunna Azrag, Mohammed, Tuty Asmawaty, Abdul Kadir, Noorlin, Mohd Ali

    Published 2020
    “…The experimental results illustrated the differences in the performances of both algorithms toward the optimum solution. At the end of the experiments, the PSO algorithm quickly convergence towards the optimum solution using a few particles and iterations rather than GABO. …”
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  13. 13
    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems

    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

    Published 2022
    “…Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. …”
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  14. 14
    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO)

    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO) by Mohd Erwan Mazalan

    Published 2022
    “…The minimization of system execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed algorithm are considered as objective functions. …”
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    Academic Exercise
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  15. 15
    Single-objective and multi-objective optimization algorithms based on sperm fertilization procedure / Hisham Ahmad Theeb Shehadeh

    Single-objective and multi-objective optimization algorithms based on sperm fertilization procedure / Hisham Ahmad Theeb Shehadeh by Hisham Ahmad, Theeb Shehadeh

    Published 2018
    “…At the end, the proposed MOSFP has been used to solve a real-life problem such as optimizing a set of Quality of Services (QoS) objective functions (network models) in WSN. …”
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  16. 16
    Solving 0/1 Knapsack Problem Using Hybrid HS and Jaya Algorithms

    Solving 0/1 Knapsack Problem Using Hybrid HS and Jaya Algorithms by Alomoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Kamal Z., Zamli

    Published 2018
    “…HS algorithm has been modified to handle the 0/1 Knapsack problem, such as adding penalty function to cope the weight condition, exclude the harmony search bandwidth (bw) parameter, and use the current best result in the next iteration to obtain a better result. …”
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  17. 17
    Optimal power flow using hybrid firefly and particle swarm optimization algorithm

    Optimal power flow using hybrid firefly and particle swarm optimization algorithm by Khan, Abdullah, Hizam, Hashim, Abdul Wahab, Noor Izzri, Othman, Mohammad Lutfi

    Published 2020
    “…In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. …”
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  18. 18
    Enhancing three variants of harmony search algorithm for continuous optimization problems

    Enhancing three variants of harmony search algorithm for continuous optimization problems by Alomoush, Alaa A., Alsewari, Abdulrahman A., Kamal Z., Zamli, Alrosan, Ayat, Alomoush, Waleed, Alissa, Khalid

    Published 2021
    “…A convergence rate graph is also used to show the overall performance of the new algorithms.…”
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  19. 19
    Fuzzy adaptive emperor penguin optimizer for global optimization problems

    Fuzzy adaptive emperor penguin optimizer for global optimization problems by Md Abdul, Kader

    Published 2023
    “…The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. …”
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  20. 20
    Common benchmark functions for metaheuristic evaluation: a review

    Common benchmark functions for metaheuristic evaluation: a review by Hussain, Kashif, Mohd Salleh, Mohd Najib, Shi, Cheng, Naseem, Rashid

    Published 2017
    “…In literature, benchmark test functions have been used for evaluating performance of metaheuristic algorithms. …”
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