A hybrid heuristic to solve a task allocation problem

Chen, Wun-Hwa; Lin, Chin–Shien

doi:10.1016/s0305-0548(99)00045-3

Cited by 60 publications

(35 citation statements)

References 20 publications

(32 reference statements)

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“…The noise method guiding the heuristic search produces to explore the solution space has led to the proposal of the recent combinatorial optimization metaheuristics technique [38], which has been applied to K-means clustering [22], as well as other application fields of the noising method such as task allocation [39], and the clique partitioning problem [40]. In addition, the noise method considers the optimal results as the outcome of a series of fluctuating data converging towards the genuine ones, and the features and the variants of the noise method are detailed, the tunings of their parameters when are applied to different combinatorial optimization problems have been summarized in [36].…”

Section: Initial Population Using Noise and K-means++ Methodsmentioning

confidence: 99%

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

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Rapidly growing Global Positioning System (GPS) data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices). In order to employ K-means to mine the better origins and destinations (OD) behind the GPS data and overcome its shortcomings including slowness of convergence, sensitivity to initial seeds selection, and getting stuck in a local optimum, this paper proposes and focuses on a novel niche genetic algorithm (NGA) with density and noise for K-means clustering (NoiseClust). In NoiseClust, an improved noise method and K-means++ are proposed to produce the initial population and capture higher quality seeds that can automatically determine the proper number of clusters, and also handle the different sizes and shapes of genes. A density-based method is presented to divide the number of niches, with its aim to maintain population diversity. Adaptive probabilities of crossover and mutation are also employed to prevent the convergence to a local optimum. Finally, the centers (the best chromosome) are obtained and then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on taxi GPS data sets demonstrate that NoiseClust has high performance and effectiveness, and easily mine the city's situations in four taxi GPS data sets.

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Section: Initial Population Using Noise and K-means++ Methodsmentioning

confidence: 99%

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

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“…A further research indicates by Gao et al, [17] that the behavior model for ant colony to choose path for food can be presented in (1).…”

Section: Pheromone-based Coordination Mecha-nismmentioning

confidence: 99%

“…The purpose of task allocation problem is to assign tasks to processors or machine such that some objective function can be maximized or minimized subject to some constraints [1]. There are many methods to solve this kind problem, such as graph theory techniques [2], branch-and-bound [3], genetic algorithm [4], tabu search [5], simulated annealing [6], and neural network [7].…”

Section: Introductionmentioning

confidence: 99%

“…Sinclair et al, [10] and Billionnet [11] use branch-and-bound algorithms to solve the task allocation problem in a heterogeneous multi-processor system with no capacity constraints. A hybrid heuristic was used to solve a task allocation problem in [1]. However, today, for a specific problem, no technique seems to be more appropriate than another.…”

Section: Introductionmentioning

confidence: 99%

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Research on Co-ordination Method for Task Allocation

Yu¹

2014

TOAUTOCJ

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Abstract:In this paper, a coordination method to solve task allocation problems in manufacturing system is proposed. inspired by the ability of the ants to establish the shortest route from their nests to the food source and back, researchers found out that these ants secrete a substance called 'pheromone' and use its trails as a medium for communicating information among each other. The pheromone-based static and dynamic coordination algorithms for task allocation are also presented. Experimental results demonstrate that this method is effective and therefore it can provide a feasible way for dealing with an actual allocation problem of production tasks in manufacturing areas.

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“… Clustering algorithm reduces the communication delay by grouping heavily communicating tasks to the same cluster and assigning them to same resource. [7]  Meta-heuristics inspired by natural phenomena include genetic algorithm [8,9], simulated annealing [10], tabu search [11], ant colony optimization [12], Particle swarm optimization [13] are also used to address the challenges of the problem. Particle swarm optimization introduced by Kennedy and Eberhart [14] simulates the swarm of birds.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure

Garg¹,

Singh²

2013

IJCNIS

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-The problem of scheduling dependent tasks (DAG) is an important version of scheduling, to efficiently exploit the computational capabilities of grid systems. The problem of scheduling tasks of a graph onto a set of different machines is an NP Complete problem. As a result, a number of heuristic and meta-heuristic approaches are used over the years due to their ability of providing high quality solutions with reasonable computation time. Discrete Particle Swarm Optimization is one such meta-heuristic used for solving the discrete problem of grid scheduling, but this method converge to sub optimal solutions due to premature convergence. To deal with premature convergence, in this paper we proposed the design and implementation of hierarchical discrete particle swarm optimization (H-DPSO) for dependent task scheduling in grid environment. In H-DPSO particles are arranged in dynamic hierarchy where good particles lying above in hierarchy are having larger influence on the swarm. We consider the bi-objective version of problem to minimize makespan and total cost simultaneously as the optimization criteria. The H-DPSO based scheduler was evaluated under different application task graphs. Simulation analysis manifests that H-DPSO based scheduling is highly viable and effective approach for grid computing.

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A hybrid heuristic to solve a task allocation problem

Cited by 60 publications

References 20 publications

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Research on Co-ordination Method for Task Allocation

Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure

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