IEEE Transactions on Computers
Real-Time Systems
Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Hybrid Genetic Algorithms for Scheduling Partially Ordered Tasks in a Multi-Processor Environment
RTCSA '99 Proceedings of the Sixth International Conference on Real-Time Computing Systems and Applications
SSST '96 Proceedings of the 28th Southeastern Symposium on System Theory (SSST '96)
A Proportional-Share Scheduler for Multimedia Applications
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
A Genetic Algorithm for Scheduling Tasks in a Real-Time Distributed System
EUROMICRO '98 Proceedings of the 24th Conference on EUROMICRO - Volume 2
Optimal Quantization of Periodic Task Requests on Multiple Identical Processors
IEEE Transactions on Parallel and Distributed Systems
Genetic-algorithm-based real-time task scheduling with multiple goals
Journal of Systems and Software - Special issue: Computer systems
Minimum and Maximum Utilization Bounds for Multiprocessor Rate Monotonic Scheduling
IEEE Transactions on Parallel and Distributed Systems
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Expert Systems with Applications: An International Journal
Real-time task scheduling by multiobjective genetic algorithm
Journal of Systems and Software
Engineering Applications of Artificial Intelligence
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
A berth allocation planning problem with direct transshipment consideration
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
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The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The proposed algorithm has a multiobjective to minimize the total tardiness and completion time simultaneously. For these conflicting objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of the proposed algorithm are better than that of other algorithms.