Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Optimizing task schedules using an artificial immune system approach
Proceedings of the 10th annual conference on Genetic and evolutionary computation
NP-complete scheduling problems
Journal of Computer and System Sciences
GPU-based island model for evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Handbook of Memetic Algorithms
Handbook of Memetic Algorithms
Efficient local search on the GPU-Investigations on the vehicle routing problem
Journal of Parallel and Distributed Computing
Solving very large instances of the scheduling of independent tasks problem on the GPU
Journal of Parallel and Distributed Computing
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Hybrid metaheuristics have shown their capabilities to solve NP-hard problems. However, they exhibit significantly higher execution times in comparison to deterministic approaches. Parallel techniques are usually leveraged to overcome the execution time bottleneck for various metaheuristics. Recently, GPUs have emerged as general purpose parallel processors and have been harnessed to reduce the execution time of these algorithms. In this work, we propose a novel parallel memetic algorithm which is fully offloaded onto GPUs. In addition, we propose an adaptive sorting strategy in order to achieve maximum possible speedups for discrete optimization problems on GPUs. In order to show the efficacy of our algorithm, a task scheduling problem for heterogeneous environments is chosen as a case study. The output of this problem can have a tangible impact on overall performance of parallel heterogeneous platforms. The achieved results of our approach are promising and show up to 696x speedup in comparison to the sequential approach for various versions of this problem. Moreover, the effects of key parameters of memetic algorithms in terms of execution time and solution quality are investigated.