Enhanced Memetic Algorithm for Task Scheduling

Scheduling tasks onto the processors of a parallel system is a crucial part of program parallelization. Due to the NP-hardness of the task scheduling problem, scheduling algorithms are based on heuristics that try to produce good rather than optimal schedules. This paper proposes a Memetic algorithm with Tabu search and Simulated Annealing as local search for solving Task scheduling problem considering communication contention. This problem consists of finding a schedule for a general task graph to be executed on a cluster of workstations and hence the schedule length can be minimized. Our approach combines local search (by self experience) and global search (by neighboring experience) possessing high search efficiency. The proposed approach is compared with existing list scheduling heuristics. The numerical results clearly indicate that our proposed approach produces solutions which are closer to optimality and/or better quality than the existing list scheduling heuristics.

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