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TASK SCHEDULING ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION (PSO) AND INVASIVE WEED OPTIMIZATION TO EXECUTE TASKS IN OVERLOADED SITUATION FOR PREEMPTIVE SYSTEM

So many studies have been done in order to execute all the tasks in real-time scheduler systems. However, different researcher are tried to tackle overload situation in real-time systems by using swarm algorithm. These studies have been categorized based on the various parameters which are important in real-time systems. As an instance, system cost, processor waiting time, number of tasks, balance use of system and etc. By increasing number of the task in task set, process time will be increased. In this situation, processor waiting time will be high when the number of the task increased and as result system cost is raising. To solve mentioned issue the authors proposed a task scheduler which is used PSO algorithm in order to cover deficiencies of previous studies in overloaded situation. This algorithm is suggested for preemptive tasks in uniprocessor in real-time systems. The result of the research has been shown PSO perform better while other common scheduling algorithm same as EDF and ACO are being over loaded. The authors by combine PSO and Invasive Weed Optimization (IWO) suggest a new algorithm that is called HPI algorithm which can perform better than PSO and schedule more tasks in overload situation.