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  1. 1
    DACS3:Embedding Individual Ant Behavior in Ant Colony System

    DACS3:Embedding Individual Ant Behavior in Ant Colony System by Ali Othman, Zulaiha, Md Rais, Helmi, Hamdan, Abdul Razak

    Published 2008
    “…Ants are fascinating creatures that demonstrate the ability to find food and bring it back to their nest. Their ability as a colony, to find paths to food sources has inspired the development of algorithms known as Ant Colony Systems (ACS). …”
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  2. 2
    DACS3: Embedding Malaysian Individual Ant Behavior in Ant Colony System

    DACS3: Embedding Malaysian Individual Ant Behavior in Ant Colony System by Ali Othman, Zulaiha, Md Rais, Helmi, Hamdan, Abdul Razak

    Published 2009
    “…Ants are fascinating creatures that demonstrate the ability to find food and bring it back to their nest. Their ability as a colony, to find paths to food sources has inspired the development of algorithms known as Ant Colony Systems (ACS). …”
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  3. 3
    The design and applications of the african buffalo algorithm for general optimization problems

    The design and applications of the african buffalo algorithm for general optimization problems by Odili, Julius Beneoluchi

    Published 2017
    “…Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. …”
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  4. 4
    Improved Dynamic Ant Colony System (DACS) on Symmetric Traveling Salesman Problem (TSP).

    Improved Dynamic Ant Colony System (DACS) on Symmetric Traveling Salesman Problem (TSP). by Md Rais, Helmi, Ali Othman, Zulaiha, Hamdan, Abdul Razak

    Published 2007
    “…Ants are a fascinating creature that demonstrates a capability of finding food and bring it back to their nest. Their ability as a colony to find paths or routes to the food sources has inspired the development of an algorithm namely Ant Colony System (ACS). …”
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  5. 5
    Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm

    Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm by Muthana, Shatha Abdulhadi, Ku Mahamud, Ku Ruhana

    Published 2023
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  6. 6
    Solving economic dispatch using ant colony optimization (ACO) / Nur Hazima Faezaa Ismail

    Solving economic dispatch using ant colony optimization (ACO) / Nur Hazima Faezaa Ismail by Ismail, Nur Hazima Faezaa

    Published 2006
    “…The inspiring source of ACO is the pheromone trail laying and following behavior of real ants which use pheromones as a communication medium. In analogy to the biological example, ACO is based on the indirect communication of a colony of simple agents, called (artificial) ants, mediated by (artificial) pheromone trails. …”
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  7. 7
    Reducing Iteration Using Candidate List

    Reducing Iteration Using Candidate List by Md Rais, Helmi, Ali Othman, Zulaiha, Hamdan, Abdul Razak

    Published 2008
    “…Ants are a fascinating creature that demonstrates a capability of finding food and bring it back to their nest. Their ability as a colony to find paths or routes to the food sources has inspired a newly developed algorithm called Dynamic Ant Colony System 3 Level Updates (DACS3). …”
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  8. 8
    A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem

    A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem by Ramli, Razamin, Abd Rahman, Rosshairy, Rohim, Nurdalila

    Published 2019
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  9. 9
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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  10. 10
    Scheduling jobs in computational grid using hybrid ACS and GA approach

    Scheduling jobs in computational grid using hybrid ACS and GA approach by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

    Published 2014
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  11. 11
    Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal

    Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal by Fadzal, Ahmad Nazmi

    Published 2017
    “…Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. …”
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  12. 12
    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation by Alazzawi, A.K., Rais, H.M., Basri, S., Alsariera, Y.A., Capretz, L.F., Imam, A.A., Balogun, A.O.

    Published 2021
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  13. 13
    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation by Alazzawi, A.K., Rais, H.M., Basri, S., Alsariera, Y.A., Capretz, L.F., Imam, A.A., Balogun, A.O.

    Published 2021
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  14. 14
    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
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  15. 15
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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  16. 16
    Development of seven segment display recognition using TensorFlow on Raspberry Pi

    Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

    Published 2022
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  17. 17
    A Modified ACO-based Search Algorithm for Detecting Protein Functional Module From Protein Interaction Network

    A Modified ACO-based Search Algorithm for Detecting Protein Functional Module From Protein Interaction Network by Jamaludin, Sallim, Rozlina, Mohamed, Roslina, Abd Hamid

    Published 2015
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  18. 18
    Seamless vertical handover technique for vehicular ad-hoc networks using artificial bee colony-particle swarm optimisation

    Seamless vertical handover technique for vehicular ad-hoc networks using artificial bee colony-particle swarm optimisation by Abdulwahhab, Mohanad Mazin

    Published 2019
    “…However, they are facing many challenges that require solutions so that wireless communication systems are able to coexist with each other effectively. …”
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  19. 19
    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network

    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network by Abduh Kaid, Monir Abdullah

    Published 2005
    “…This routing problem is an NPhard problem. Many algorithms are designed by many researchers to perform this routing such as window method, sequential algorithm, degree-descending algorithm, simulated annealing algorithm, genetic algorithm and ant colony algorithm.This thesis explores two approaches, sequential and parallel approaches. …”
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  20. 20
    Ant system with heuristics for capacitated vehicle routing problem

    Ant system with heuristics for capacitated vehicle routing problem by Tan, Wen Fang

    Published 2013
    “…As a result, the ASH algorithm is a viable alternative for addressing the CVRP with satisfactory solution quality and run time.…”
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