Search Results - optimal ((colony algorithm) OR (((((rsa algorithm) OR (new algorithm))) OR (graph algorithm))))

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

“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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African Buffalo Optimization (ABO): A New Metaheuristic Algorithm by Odili, Julius Beneoluchi, M. N. M., Kahar

“…When compared to the Ant Colony Optimization algorithm, Simulated Annealing and Genetic Algorithm, the results obtained from African Buffalo Optimization show that the algorithm works well and can be extended to solving problems like: path planning, scheduling, vehicle routing in addition to other constraint-driven problems.…”
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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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Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling by Shimailawi, Shatha Abdulhadi Muthana

“…Friedman test using GRG metric shows significant better performance (p-values<0.05) for PACS algorithm compared to benchmark algorithms. The proposed models and algorithm can be used to solve the multi-objective GMS problem while the new parameters’ values can be used to obtain optimal or near optimal maintenance scheduling of generators. …”
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A Hybrid ACO-Graph Entropy for Functional Modules Detection From Protein-Protein Interaction Network by Jamaludin, Sallim, Rozlina, Mohamed, Che, Yahaya, Roslina, Abdul Hamid

“…The Ant Colony Optimization (ACO) Algorithm has been adapted for the protein functional module detection by modeling the problem as an optimization problem. …”
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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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A Modified ACO-based Search Algorithm for Detecting Protein Functional Module From Protein Interaction Network by Jamaludin, Sallim, Rozlina, Mohamed, Roslina, Abd Hamid

“…The Ant Colony Optimization (ACO) Algorithm has been adapted for the protein functional module detection by modeling the problem as an optimization problem. …”
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Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences by Rajesvary Rajoo R., Salam R.A.

Subjects: “…Ant colony optimization…”

Benchmark simulator with dynamic environment for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana

“…Characteristics of jobs and resources to be used in evaluating the performance of the scheduling algorithm must reflect the dynamic nature of real grid environment.Static models of jobs and resources cannot be used to generate jobs and resources in simulating the grid environment because of the dynamic nature of the grid.This paper presents a new graph representation of jobs and resources which is practical for hybrid metaheuristic model implementation such as ant colony optimization and genetic algorithm.A dynamic model that can generate jobs and resources similar to the jobs and resources in the real grid environment is also proposed.Jobs and resources may join in or drop out from the grid.Stochastic analysis is performed on the characteristics of jobs and resources.A simulator based on the dynamic expected time to compute, has been developed and can be used as a benchmark.The simulator can generate jobs and resources with the characteristics of jobs and resources in the real grid environment.This will facilitates the evaluation of dynamic job scheduling algorithm.…”
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A new multiple ant colonies optimization algorithm utilizing average pheromone evaluation mechanism by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md Norwawi, Norita

“…Multiple ant colonies optimization is an extension of the Ant Colony Optimization framework It offers a good opportunity to improve the ant colony optimization algorithms by encouraging the exploration of a wide area of the search space without losing the chance of exploiting the history of the search.This paper proposes a new multiple ant colonies optimization algorithm that is based on ant colony system and utilizes ave rage pheromone evaluation mechanism.The new algorithm divides the ants’ populations into multiple ant colonies and can be used to tackle large volume combinatorial optimization problems effectively. …”
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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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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

“…This approach offers good opportunity to explore a large area of the search space. This paper proposes a new generic algorithmic approach that utilized multiple ant colonies with several new interaction techniques. …”
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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

“…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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Artificial bee colony algorithm with proposed discrete nearest neighborhood algorithm for discrete optimization problems by Rahimi, Amir Masoud, Ramezani-Khansari, Ehsan

“…Travelling salesman problem (TSP) is one the problems of NP-complete family, which means finding shortest complete close tour in the graph. This study seeks to solve this problem using Artificial Bee Colony (ABC) Algorithm along with the proposed Discrete Nearest Neighborhood Algorithm (DNNA). …”
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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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A hybrid algorithm based on artificial bee colony and artificial rabbits optimization for solving economic dispatch problem by Lee, Wei Wen, Hashim, Mohd Ruzaini

“…The new hybrid algorithm integrates the good features of both standard optimization strategies, thus producing better possible solutions. …”
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Path Optimization For Cooperative Multi-Head 3d Printing by Cheong, Kah Jun

“…Two well-known algorithms for solving a closely related graph theory problem known as the Travelling Salesman Problem (TSP) are the Ant System (AS) and Ant Colony System (ACS) algorithms. …”
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Interacted Multiple Ant Colonies for Search Stagnation Problem by Aljanabi, Alaa Ismael

“…This thesis addresses the issues associated with search stagnation problem that ACO algorithms suffer from. In particular, it proposes the use of multiple interacted ant colonies as a new algorithmic framework. …”
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Seed disperser ant algorithm for optimization / Chang Wen Liang by Chang , Wen Liang

“…New young queens are produced by the colony that migrates to establish new colonies after local optimum solution reached to start new local search. …”
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Optimizing boarding school schedule using graph colouring : case study of Sekolah Menengah Sultan Abdul Halim / Nur Farhana Mohd Asri by Mohd Asri, Nur Farhana

“…The minimum color represents the minimum time slots required to construct the new schedule. The result has shown that the Greedy Algorithm has succeeded to produce the lowest minimal color compared to Graph Coloring Algorithm. …”
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