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  1. 1
    Simulation of identifying shortest path walkway in library by using ant colony optimization

    Simulation of identifying shortest path walkway in library by using ant colony optimization by Chui Teng, Chan

    Published 2012
    “…A research is proposed based on Ant Colony Optimization for solving the shortest path problem in library.This is a research that the algorithm is aim to implement on a robot. …”
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  2. 2
    Optimization of multi-holes drilling path using particle swarm optimization

    Optimization of multi-holes drilling path using particle swarm optimization by Najwa Wahida, Zainal Abidin

    Published 2022
    “…The performance of PSO was then compared with other meta-heuristic algorithms, including Genetic Algorithm (GA) and Ant Colony Optimisation (ACO), Whale Optimisation Algorithm (WOA), Ant Lion Optimiser (ALO), Dragonfly Algorithm (DA), Grasshopper Optimisation Algorithm (GOA), Moth Flame Optimisation (MFO) and Sine Cosine Algorithm (SCA). …”
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  3. 3
    A comparative study on ant-colony algorithm and genetic algorithm for mobile robot planning.

    A comparative study on ant-colony algorithm and genetic algorithm for mobile robot planning. by Rajendran, Piraviendran, Othman, Muhaini

    Published 2024
    “…Focusing on routing optimization, the study evaluates Ant-Colony Optimization (ACO) and Genetic Algorithm (GA) in Mobile Robot Planning. …”
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  4. 4
    A Kalman Filter Approach to PCB Drill Path Optimization Problem

    A Kalman Filter Approach to PCB Drill Path Optimization Problem by Abdul Aziz, Nor Hidayati, Zuwairie, Ibrahim, Ab. Aziz, Nor Azlina, Saifudin, Razali, Abas, Khairul Hamimah, Mohamad, Mohd Saberi

    Published 2017
    “…Drill path optimization problem is an important problem in holes drilling with computer numerically controlled (CNC) machine. …”
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  5. 5
    DNA sequence design for direct-proportional length-based DNA computing: Particle swarm optimization vs population based ant colony optimization

    DNA sequence design for direct-proportional length-based DNA computing: Particle swarm optimization vs population based ant colony optimization by Zulkifli, Md. Yusof, Muhammad Arif, Abdul Rahim, Sophan Wahyudi, Nawawi, Kamal, Khalil, Zuwairie, Ibrahim

    Published 2012
    “…Previously, direct-proportional length-based DNA computing which involved DNA sequences with different lengths has been implemented to solve the shortest path problem. In this study, particle swarm optimization (PSO) and population-based ant colony optimization (P-ACO) are employed to design DNA sequences with different lengths and the results obtained are compared. …”
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  6. 6
    Enhancement of Ant System Algorithm for Course Timetabling Problem

    Enhancement of Ant System Algorithm for Course Timetabling Problem by Djamarus, Djasli

    Published 2009
    “…As the requirement of the Ant System Algorithm, the problem is modeled as a graph that can be used by the ant to deliver its pheromone. …”
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  7. 7
    Ant colony optimization in dynamic environments

    Ant colony optimization in dynamic environments by Chen, Fei Huang

    Published 2010
    “…In order to achieve this objective, six ant algorithms namely Ant System (AS), Ant Colony System (ACS), Best-Worst Ant System (BWAS), Elitist Ant System (EAS), Max-Min Ant System (MMAS) and Rank-Based Ant System (RBAS) were implemented to solve a dynamic optimization problem in the form of the dynamic Traveling Salesman Problem (TSP). …”
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  8. 8
    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
    “…In computational grid, job scheduling is one of the main factors affecting grid computing performance. 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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  9. 9
    AntNet: a robust routing algorithm for data networks

    AntNet: a robust routing algorithm for data networks by Haseeb, Shariq, Sidek, Khairul Azami, Ismail, Ahmad Faris, Weng Kin, Lai, Yit Mei, Aw

    Published 2004
    “…AntNet is an innovative algorithm that may be used for data networks. …”
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  10. 10
    Ant colony optimization for solving solid waste collection scheduling problems

    Ant colony optimization for solving solid waste collection scheduling problems by Ismail, Zuhaimy, Loh, S. L.

    Published 2009
    “…Approach: The implementation of Ant Colony Optimization (ACO) for solving solid waste collection problem as a VRPSD model was described. …”
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  11. 11
    An Efficient Improvement Of Ant Colony System Algorithm For Handling Capacity Vehicle Routing Problem

    An Efficient Improvement Of Ant Colony System Algorithm For Handling Capacity Vehicle Routing Problem by Modhi Lafta Mutar, Mohd Aboobaider, Burhanuddin, Asaad Shakir Hameed, Yusof, Norzihani, Hussein Jameel Mutashar

    Published 2020
    “…This paper presents an improvement of Ant Colony System (ACS) to solve this problem. In this study, the problem deals with a few vehicles which are used for transporting products to specific places. …”
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  12. 12
    Application of Moth-Flame Optimizer and Ant Lion Optimizer to Solve Optimal Reactive Power Dispatch Problems

    Application of Moth-Flame Optimizer and Ant Lion Optimizer to Solve Optimal Reactive Power Dispatch Problems by Rebecca Ng, Shin Mei, M. H., Sulaiman, Hamdan, Daniyal, Zuriani, Mustaffa

    Published 2018
    “…This paper presents the application of two nature-inspired meta-heuristic algorithms, namely moth-flame optimizer (MFO) and ant lion optimizer (ALO) in obtaining the optimal settings of control variables for solving optimal reactive power dispatch (ORPD) problems. …”
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  13. 13
    Chaotic ant swarm optimization for economic dispatch considering transmission losses / Amelia Abd Razak

    Chaotic ant swarm optimization for economic dispatch considering transmission losses / Amelia Abd Razak by Abd Razak, Amelia

    Published 2012
    “…The proposed method is tested on six generation unit systems and the results show that it is able to solve ED problem. CASO algorithm used in this study was implemented by using MATLAB 7.5.0 (R2007b). …”
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  14. 14
    Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

    Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing by Alobaedy, Mustafa Muwafak Theab

    Published 2015
    “…One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. …”
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  15. 15
    Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

    Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems by Ismail, Zuhaimy, Loh, Ser Lee

    Published 2009
    “…Approach: The implementation of Ant Colony Optimization (ACO) for solving solid waste collection problem as a VRPSD model was described. …”
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  16. 16
    An Ant Colony System for Solving DNA Sequence Design Problem in DNA Computing

    An Ant Colony System for Solving DNA Sequence Design Problem in DNA Computing by Farhaana, Yakop, Zuwairie, Ibrahim, Amar Faiz, Zainal Abidin, Zulkifli, Md. Yusof, Mohd Saberi, Mohamad, Khairunizam, Wan, Watada, Junzo

    Published 2013
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  17. 17
    Optimization of multi-holes drilling path using particle swarm optimization

    Optimization of multi-holes drilling path using particle swarm optimization by Najwa Wahida, Zainal Abidin, M. F. F., Ab Rashid, N. M. Zuki, N. M.

    Published 2018
    “…PSO is also compared with another algorithm like Whale Optimization Algorithm, Ant Lion Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Moth-flame Optimization and Sine Cosine Algorithm. …”
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  18. 18
    Strategic oscillation for exploitation and exploration of ACS algorithm for job scheduling in static grid computing

    Strategic oscillation for exploitation and exploration of ACS algorithm for job scheduling in static grid computing by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

    Published 2015
    “…Exploitation and exploration mechanisms are the main components in metaheuristics algorithms. These mechanisms are implemented explicitly in ant colony system algorithm.The rate between the exploitation and exploration mechanisms is controlled using a parameter set by the users of the algorithm. …”
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  19. 19
    Optimal robot path planning using enhanced particle swarm optimization algorithm

    Optimal robot path planning using enhanced particle swarm optimization algorithm by Mohammed, Athraa Jasim, Ghathwan, Khalil Ibrahim, Yusof, Yuhanis

    Published 2020
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  20. 20
    Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system

    Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system by Mohammad Ata, Karimeh Ibrahim, Che Soh, Azura, Ishak, Asnor Juraiza, Jaafar, Haslina

    Published 2021
    “…This solution depending on applying the optimization on an optimal path while the traditional ACO is optimizing the random path based on the greedy algorithm hence we get the most optimal path. …”
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