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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

“…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
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Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high.This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system.The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism.The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element.The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job.EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form.The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. …”
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Ant colony algorithm for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana, Abdul Nasir, Husna Jamal

“…Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources.This will lead to resourccs having high workload and stagnation may occur if computational times of the processed jobs are high.This paper proposed an enhanced ant colony optimization algorithm for jobs and resources scheduling in grid computing.The proposed ant colony algorithm for job scheduling in the grid environment combines the techniques from Ant Colony System and Max - Min Ant System.The algorithm focuses on local pheromone trail update and the trail limit values. …”
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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

“…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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Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

“…Hybrid metaheuristic algorithms have the ability to produce better solution than stand-alone approach and no algorithm could be concluded as the best algorithm for scheduling algorithm or in general, for combinatorial problems.This study presents the low and high level hybridization of ant colony system and genetic algorithm in solving the job scheduling in grid computing.Two hybrid algorithms namely ACS(GA) as a low level and ACS+GA as a high level are proposed.The proposed algorithms were evaluated using static benchmarks problems known as expected time to compute model. …”
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Scheduling jobs in computational grid using hybrid ACS and GA approach by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

“…Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems.However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time.This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to solve the job scheduling problem.The high level hybridization algorithm will keep the identity of each algorithm in performing the scheduling task.The study focuses on static grid computing environment and the metrics for optimization are the makespan and flowtime.Experiment results show that the proposed algorithm outperforms other stand-alone algorithms such as ant system, genetic algorithms, and ant colony system for makespan.However, for flowtime, ant system and genetic algorithm perform better.…”
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Interacted multiple ant colonies optimization approach to enhance the performance of ant colony optimization algorithms by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md. Norwawi, Norita

“…The proposed approach outperforms the single colony ant algorithms in term of solution quality with the same computational effort.…”
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Comparison between ant colony and genetic algorithm using traveling salesman problem by Abduljabbar, Zaid Ammen, Khalefa, Mustafa S., A. Jabar, Marzanah

“…The results of comparison show that ant colony is high efficient than genetic algorithm and it requires less computational cost and generally only a few lines of code.…”
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Adaptive parameter control strategy for ant-miner classification algorithm by Al-Behadili, Hayder Naser Khraibet, Sagban, Rafid, Ku-Mahamud, Ku Ruhana

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Optimizing large scale combinatorial problems using multiple ant colonies algorithm based on pheromone evaluation technique by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md Norwawi, Norita

“…The new algorithm can effectively be used to tackle large scale optimization problems.Computational tests show promises of the new algorithm.…”
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Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian by Ahmad Yusri, Nur Aisyatul Husna, Saian, Rizauddin

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New heuristic function in ant colony system for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana, Alobaedy, Mustafa Muwafak

“…Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Ant colony system algorithm is a 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 paper focuses on enhancing the heuristic function where information about recent ants’ discoveries will be taken into account.Experiments were conducted using a simulator with dynamic environment features to mimic the grid environment.Results show that the proposed enhanced algorithm produce better output in term of utilization and make span.…”
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Enhancement of Ant System Algorithm for Course Timetabling Problem by Djamarus, Djasli

“…This research starts with developing an algorithm based on original concept of Ant System Algorithm. …”
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A novel algorithm : apriori-ant. The combination of apriori algorithm and ants colony optimization algorithm by Choo, Ai Ling

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A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules by Rizauddin, Saian

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Heuristic factors in ant system algorithm for course timetabling problem by Djamarus, Djasli, Ku-Mahamud, Ku Ruhana

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Interacted multiple ant colonies optimization framework: An experimental study of the evaluation and the exploration techniques to control the search stagnation by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md. Norwawi, Norita

“…The performance of IMACO was demonstrated by comparing it with the best performing ant algorithms like Ant Colony System (ACS) and Max-Min Ant System (MMAS). …”
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An Enhanced Ant Colony Optimisation Algorithm with the Hellinger Distance for Shariah-Compliant Securities Companies Bankruptcy Prediction by Zainol, Annuur Zakiah, Saian, Rizauddin, Teoh, Yeong Kin, Mohd Razali, Muhammad Hasbullah, Abu Bakar, Sumarni

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Stagnation control using interacted multiple ant colonies by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md Norwawi, Norita

“…Computational tests show that the proposed approach is competitive with other state of art ant algorithms.…”
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