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Task scheduling is critical for obtaining a high performance schedule in heterogeneous computing systems (HCS) and searching an optimal scheduling solution has been shown to be NP-complete. In this paper, a hybrid heuristicgenetic algorithm with adaptive parameter (HGAAP) is proposed by combining a heuristic scheduling algorithm and a genetic algorithm. An existing common heuristic scheduling algorithm...
A hybrid optimization algorithm is proposed for Job-Shop scheduling problem, which is based on the combination of adaptive genetic algorithm and improved ant algorithm. The algorithm gets the initial pheromone distribution using adaptive genetic algorithm at first, then runs improved ant algorithm. The algorithm utilizes the advantages of the two algorithms and overcomes their disadvantages. Experimental...
The studied problem is to optimize a production system where these systems have two dedicated processors. The assignment of tasks to these processors is fixed. For this problem, we have three types of tasks. Some tasks must be processed only by the first processor, a few others by the second processor and the remaining tasks need simultaneously both processors. This NP-hard problem requires the use...
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...
The problem of scheduling independent users' jobs to resources in Grid Computing systems is of paramount importance. This problem is known to be NP-hard, and many techniques have been proposed to solve it, such as heuristics, genetic algorithms (GA), and, more recently, particle swarm optimization (PSO). This article aims to use PSO to solve grid scheduling problems, and compare it with other techniques...
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