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  1. 1
    Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics

    Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics by Mohammad Jarrah, Mu'ath Ibrahim

    Published 2018
    “…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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  2. 2
    Development of committee machine models for multiple response optimization problems

    Development of committee machine models for multiple response optimization problems by Golestaneh, Seyed Jafar

    Published 2014
    “…Four methodologies are to make four different CM models to solve MRO problems. The fifth methodology proposes the final algorithm which uses four CM models together to solve MRO problems. …”
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  3. 3
    Route optimization using shortest path method / Muhamad Faisal Amin Shakri

    Route optimization using shortest path method / Muhamad Faisal Amin Shakri by Shakri, Muhamad Faisal Amin

    Published 2025
    “…As the algorithm optimization is crucial for the sustainability and effective logistic operation, this study concludes that A* algorithm is the most efficient in this case.…”
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  4. 4
    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 performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. …”
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  5. 5
    Meta-heuristic structure for multiobjective optimization case study: Green sand mould system

    Meta-heuristic structure for multiobjective optimization case study: Green sand mould system by Ganesan, T., Elamvazuthi, I., KuShaari, K.Z., Vasant, P.

    Published 2014
    “…This method is then used generate highly diverse and optimal solutions to the green sand mould system which is a real-world problem. …”
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  6. 6
    Optimizing boarding school schedule using graph coloring: case study of Sekolah Menengah Sultan Abdul Halim / Nur Farhana Mohd Asri

    Optimizing boarding school schedule using graph coloring: case study of Sekolah Menengah Sultan Abdul Halim / Nur Farhana Mohd Asri by Mohd Asri, Nur Farhana

    Published 2022
    “…In order to achieve this goal, the school schedule has been optimized using the Graph Coloring method. In this study, two approaches have been used under the Graph Coloring method which is Vertex and Edge Coloring and both of it have been solved by implementing the specific algorithm in it. …”
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  7. 7
    Optimizing the placement of fire department in Kulim using greedy heuristic and simplex method / Muhammad Abu Syah Mohd Suzaly

    Optimizing the placement of fire department in Kulim using greedy heuristic and simplex method / Muhammad Abu Syah Mohd Suzaly by Mohd Suzaly, Muhammad Abu Syah

    Published 2023
    “…The first method is greedy heuristic method. Greedy heuristics is a type of optimization algorithm that makes decisions based on locally optimal solutions. …”
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  8. 8
    Comparison of performances of Jaya Algorithm and Cuckoo Search algorithm using benchmark functions

    Comparison of performances of Jaya Algorithm and Cuckoo Search algorithm using benchmark functions by Ahmed, Mashuk, Nasser, Abdullah B., Kamal Z., Zamli, Heripracoyo, Sulistyo

    Published 2022
    “…Metaheuristic algorithms have been used successfully for solving different optimization problems. …”
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  9. 9
    Hybrid flow shop scheduling problem with energy utilization using non-dominated sorting genetic algorithm-III (NSGA-III) optimization

    Hybrid flow shop scheduling problem with energy utilization using non-dominated sorting genetic algorithm-III (NSGA-III) optimization by Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

    Published 2023
    “…The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). …”
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  10. 10
    Optimizing boarding school schedule using graph colouring : case study of Sekolah Menengah Sultan Abdul Halim / Nur Farhana Mohd Asri

    Optimizing boarding school schedule using graph colouring : case study of Sekolah Menengah Sultan Abdul Halim / Nur Farhana Mohd Asri by Mohd Asri, Nur Farhana

    Published 2021
    “…In order to achieve this goal, the school schedule has been optimized using the Graph Coloring method. In this study, two approaches have been used under the Graph Coloring method which is Vertex and Edge Coloring and both of it have been solved by implementing the specific algorithm in it. …”
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  11. 11
    A high-performance democratic political algorithm for solving multi-objective optimal power flow problem

    A high-performance democratic political algorithm for solving multi-objective optimal power flow problem by Ahmadipour M., Ali Z., Othman M.M., Bo R., Javadi M.S., Ridha H.M., Alrifaey M.

    Published 2025
    “…The proposed approach is tested and validated on IEEE 57-bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. …”
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  12. 12
    Hybrid optimal control-swarm intelligence for optimization of selected cancer chemotherapy model / Omar Ali Mohammad Shindi

    Hybrid optimal control-swarm intelligence for optimization of selected cancer chemotherapy model / Omar Ali Mohammad Shindi by Omar Ali , Mohammad Shindi

    Published 2018
    “…That caused increasing in the number of objectives and constraints, so increasing in the complexity of the optimization problem. This research project proposes two hybrid techniques that’s combined between the optimal control theory (OCT) with the swarm intelligence (SI) and evolutionary algorithms (EA), and check the performance of this techniques, with the popular method that used purely SI and EA algorithms, such M-MOPSO, MOPOS, MOEAD, MODE. …”
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  13. 13
    Using artificial intelligence search in solving the camera placement problem

    Using artificial intelligence search in solving the camera placement problem by Altahir, A.A., Asirvadam, V.S., Hamid, N.H.B., Sebastian, P.

    Published 2022
    “…Two case studies are used to evaluate those algorithms, and the camera placement problem is formulated as a coverage maximization problem. …”
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  14. 14
    A high-performance democratic political algorithm for solving multi-objective optimal power flow problem

    A high-performance democratic political algorithm for solving multi-objective optimal power flow problem by Ahmadipour, Masoud, Ali, Zaipatimah, Othman, Muhammad Murtadha, Bo, Rui, Javadi, Mohammad Sadegh, Ridha, Hussein Mohammed, Alrifaey, Moath

    Published 2024
    “…The proposed approach is tested and validated on IEEE 57-bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. …”
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  15. 15
    Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling

    Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling by Anuar, Nurul Izah

    Published 2022
    “…The experimentations of the proposed algorithm are conducted using existing benchmark instances and a published case study on an energy-efficient job-shop model. …”
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  16. 16
    Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm

    Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm by Badakhshian, Mostafa

    Published 2009
    “…Several experiments have been done to the proposed test cases. The results showed that tournament selection scheme may outperform roulette wheel in this problem. …”
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  17. 17
    Backtracking search algorithm for optimal power dispatch in power system / Mostafa Modiri Delshad

    Backtracking search algorithm for optimal power dispatch in power system / Mostafa Modiri Delshad by Mostafa, Modiri Delshad

    Published 2016
    “…Backtracking search algorithm (BSA) as the new evolutionary technique of optimization is used for solving the problems. …”
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  18. 18
    Product assembly sequence optimization based on genetic algorithm

    Product assembly sequence optimization based on genetic algorithm by Yasin, Azman, Puteh, Nurnasran, Daud, Ruslizam, Omar, Mazni, Syed-Abdullah, Sharifah Lailee

    Published 2010
    “…Genetic algorithm (GA) is a search technique used in computing to find approximate solution to optimization and search problem based on the theory of natural selection. …”
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  19. 19
    A genetic algorithm to minimise the maximum lateness on a single machine family scheduling problem

    A genetic algorithm to minimise the maximum lateness on a single machine family scheduling problem by Lee, Lai Soon, Nazif, Habibeh

    Published 2009
    “…The OCGA is compared with other well known local search method namely dynamic length tabu search, randomised steepest descent method, and other variants of genetic algorithms using extensive data sets collected from the literatures. …”
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  20. 20
    Information Theoretic-based Feature Selection for Machine Learning

    Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

    Published 2018
    “…In these extended IFS method, feature selection method was defined and presented as a 0-1 Knapsack Problem (MKP). …”
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