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The Ant Colony Optimization algorithms (ACO) are computational models inspired by the collective foraging behavior of ants. By looking at the strengths of ACO, they are the most appropriate for scheduling of tasks in soft real-time systems. In this paper, ACO based scheduling algorithm for real-time operating systems (RTOS) has been proposed. During simulation, results are obtained with periodic tasks,...
This paper applies the up-to-date intelligent simulation ant colony algorithm to dynamic container truck scheduling. It proposes to take in real-time traffic information through GPS and to solve the problem on container truck scheduling with consideration of the optimal capability of ant colony in the searching process of food resources. Meanwhile, it puts information entropy into ant colony algorithm...
The complexity of real-time systems has substantially increased in the past few years regarding both hardware and software aspects. The use of modern sensors, able to capture image and audio data, demands predictable multimedia-like data processing. Moreover, applications like autonomous robots, surveillance, or modern multimedia players may well be characterized by several operation modes, each one...
For a certain real-time task scheduling problems with characteristics of uncertain task sets, the executing time dynamic changing and the task's mutual coupling strength dynamic changing, a multi-tasking dynamic scheduling algorithm is proposed based on dynamic weight value compensation and adaptive task jumping, which solved the loads storming caused by the dynamic task scheduling as well as the...
EDF (earliest deadline first) has been proved to be optimal scheduling algorithm for single processor realtime operating systems when the systems are preemptive and underloaded. The limitation of this algorithm is, its performance decreases exponentially when system becomes slightly overloaded. Authors have already proved ability of ACO (Ant Colony Optimization) based scheduling algorithm for real-time...
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