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Access type: UnknownSubject: search.filters.subject.Artificial bee colony algorithm

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    Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing
    (2020-01-01)
    Kruekaew, Boonhatai
    ;
    Kimpan, Warangkhana
    This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.
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    Adaptive artificial bee colony algorithm for solving the capacitated vehicle routing problem
    (2017-03-23)
    Mingprasert, S.
    ;
    Masuchun, R.
    This research provides an adaptive artificial bee colony algorithm (AABC) to handle the goods transportation routing problem based on available capacity of vehicles well-known as Capacitated Vehicle Routing Problem (CVRP). The proposed algorithm is developed from the traditional artificial bee colony algorithm to approve a good performance and quality solution. This algorithm was verified through VRP Benchmark problems by comparing the results with existing Best Known Solutions (BKS) of the Capacitated VRP benchmark. The experimental results found that AABC can nearly confirm the good performances as compared with benchmark problem of Augerat et al. that can success the short running time and receive average nearest values.
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    A hybrid artificial bee colony algorithm with local search for flexible job-shop scheduling problem
    (2013-01-01)
    Thammano, Arit
    ;
    Phu-Ang, Ajchara
    This paper presents a hybrid artificial bee colony algorithm for solving the flexible job-shop scheduling problem (FJSP) with the criteria to minimize the maximum completion time (makespan). In solving the FJSP, we have to focus on two sub-problems: determining the sequence of the operations and selecting the best machine for each operation. In the proposed algorithm, first, several dispatching rules and the harmony search algorithm are used in creating the initial solutions. Thereafter, one of the two search techniques is randomly selected with a probability that is proportional to their fitness values. The selected search technique is applied to the initial solution to explore its neighborhood. If a premature convergence to a local optimum happens, the simulated annealing algorithm will be employed to escape from the local optimum. Otherwise, the filter and fan algorithm is utilized. Finally, the crossover operation is presented to enhance the exploitation capability. Experimental results on the benchmark data sets show that the proposed algorithm can effectively solve the FJSP. © 2013 The Authors. Published by Elsevier B.V.