Scheduling independent tasks: Bee Colony Optimization approach (original) (raw)

Bee colony optimization for scheduling independent tasks to identical processors

Journal of Heuristics, 2012

The static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. This problem is treated by the Bee Colony Optimization (BCO) meta-heuristic. The BCO algorithm belongs to the class of stochastic swarm optimization methods inspired by the foraging habits of bees in nature. To investigate the performance of the proposed method extensive numerical experiments are performed. Our BCO algorithm is able to obtain the optimal value of the objective function in the majority of test examples known from literature. The deviation of non-optimal solutions from the optimal ones in our test examples is at most 2%. The CPU times required to find the best solutions by BCO are significantly smaller than the corresponding times required by the CPLEX optimization solver. Moreover, our BCO is competitive with state-of-the-art methods for similar problems, with respect to both solution quality and running time. The stability of BCO is examined through multiple executions and it is shown that solution deviation is less than 1%.

Utilizing Bee Colony to Solve Task Scheduling Problem in Distributed Systems

2011 Third International Conference on Computational Intelligence, Communication Systems and Networks, 2011

Tasks scheduling problem is a key factor for distributed systems to gain better performance. Even in the best conditions, the scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering the techniques that can reduce the complexity of the optimization. Spending too much time for doing scheduling is considered the main shortcoming of these approaches. Therefore, in this paper memetic algorithm has been used to cope with this shortcoming. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. Extended experimental results demonstrated that the proposed method outperform the existent GA-based method in term of Makespan.

Towards Scheduling Optimization through Artificial Bee Colony Approach

In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated.

A Bee Colony Task Scheduling Algorithm in Computational Grids

Communications in Computer and Information Science, 2011

The efficient scheduling of the independent and sequential tasks on distributed and heterogeneous computing resources within grid computing environments is an NP-complete problem. Therefore, using heuristic approaches to solve the scheduling problem is a very common and also acceptable method in these environments. In this paper, a new task scheduling algorithm based on bee colony optimization approach is proposed. The algorithm uses artificial bees to appropriately schedule the submitted tasks to the grid resources. Applying the proposed algorithm to the grid computing environments, the maximum delay and finish times of the tasks are reduced. Furthermore, the total makespan of the environment is minimized when the algorithm is applied. The proposed algorithm not only minimizes the makespan of the environment, but also satisfies the deadline and priority requirements of the tasks. Simulation results obtained from applying the algorithm to different grid environments show the prominence of the algorithm to other similar scheduling algorithms.

Application and Evaluation of Bee-Based Algorithms in Scheduling

Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, 2018

Scheduling is a vital element of manufacturing processes and requires optimal solutions under undetermined conditions. Highly dynamic and, complex scheduling problems can be classified as np-hard problems. Finding the optimal solution for multi-variable scheduling problems with polynomial computation times is extremely hard. Scheduling problems of this nature can be solved up to some degree using traditional methodologies. However, intelligent optimization tools, like BBAs, are inspired by the food foraging behavior of honey bees and capable of locating good solutions efficiently. The experiments on some benchmark problems show that BBA outperforms other methods which are used to solve scheduling problems in terms of the speed of optimization and accuracy of the results. This chapter first highlights the use of BBA and its variants for scheduling and provides a classification of scheduling problems with BBA applications. Following this, a step by step example is provided for multi-m...

Independent Task Scheduling in Grid Computing Based on Queen Bee Algorithm

The inherent dynamical in grid computing has made it extremely difficult to come up with near-optimal solutions to efficiently schedule tasks in grids. Task Scheduling plays crucial role in Grid computing. It is a challenge-able issue among scientists to achieve better results especially in makespan based on various AI methods. Nowadays, non deterministic algorithms provide better results for these tasks. In this study the task scheduling problem in Grid computing environments has been addressed. In this paper, Queen Bee Algorithm is used for resolving scheduling problem and the obtained results are compared with several Meta–heuristic Algorithms which are developed to solve the problem. As it illustrated, queen bee algorithm is declined considerably makespan and execution time parameters rather than others in different states.

Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization

Soft Computing, 2012

The artificial bee colony has the advantage of employing fewer control parameters compared with other population-based optimization algorithms. In this paper a binary artificial bee colony (BABC) algorithm is developed for binary integer job scheduling problems in grid computing. We further propose an efficient binary artificial bee colony extension of BABC that incorporates a flexible ranking strategy (FRS) to improve the balance between exploration and exploitation. The FRS is introduced to generate and use new solutions for diversified search in early generations and to speed up convergence in latter generations. Two variants are introduced to minimize the makepsan. In the first a fixed number of best solutions is employed with the FRS while in the second the number of the best solutions is reduced with each new generation. Simulation results for benchmark job scheduling problems show that the performance of our proposed methods is better than those alternatives such as genetic algorithms, simulated annealing and particle swarm optimization. Keywords Artificial bee colony (ABC) Á Binary artificial bee colony (BABC) Á Efficient binary artificial bee colony (EBABC) Á Flexible ranking strategy (FRS) Á Job scheduling Á Grid computing Communicated by F. Herrera.

Multi-objective tasks scheduling using bee colony algorithm in cloud computing

International Journal of Electrical and Computer Engineering (IJECE), 2022

Due to the development of communication device technology and the need to use up-to-date infrastructure ready to respond quickly and in a timely manner to computational needs, the competition for the use of processing resources is increasing nowadays. The scheduling tasks in the cloud computing environment have been remained a challenge to access a quick and efficient solution. In this paper, the aim is to present a new tactic for allocating the available processing resources based on the artificial bee colony (ABC) algorithm and cellular automata for solving the task scheduling problem in the cloud computing network. The results show the performance of the proposed method is better than its counterparts.

Solving the Scheduling Problem in Computational Grid using Artificial Bee Colony Algorithm

Scheduling tasks on computational grids is known as NPcomplete problem. Scheduling tasks in Grid computing, means assigning tasks to resources such that the time termination and average waiting time criteria and the number of required machines are optimized. Based on heuristic or meta-heuristic search have been proposed to obtain optimal solutions. The presented method tries to optimize all of the mentioned criteria with artificial bee colony system with consideration to precedence of tasks. Bee colony optimization is one of algorithms which categorized in swarm intelligence that can be used in optimization problems. This algorithm is based on the intelligent behavior of honey bees in foraging process. The result shows using bees for solving scheduling problem in computational grid makes better finish time and average waiting time.