Certain investigations on particle Swarm optimization techniques for Multiprocessor scheduling problems

Abstract

The importance of scheduling has increased in recent years due to newlinethe growing consumer demand for variety of reduced product life cycles newline changing markets with global competition and rapid development of new newlineprocesses and technologies Two cases of the task scheduling exist in newlineliterature namely independent and dependent task scheduling The newlineindependent or dependent tasks can be static or dynamic Scheduling static newlinetasks require a priori knowledge of its execution time on the processing newlinenodes Dynamic task scheduling scheme does not require a prior knowledge newlineof the tasks to be scheduled The tasks considered for scheduling can be newlinepreemptive or non preemptive in nature The minimization of the make span newlineis the intent of the task scheduling problem The thesis work comprises a snapshot newlineof particle swarming techniques including variations in the algorithm and its applications in the newlinearea of multiprocessor scheduling Algorithm inspired from Particle Swarm newlineOptimization PSO method and its variants are successfully implemented for newlinemultiprocessor scheduling optimization problems Its efficiency for solving newlinethe problem is to minimize the make span criterion Genetic Algorithm PSO newlineand its variants are used to solve the task scheduling problem Static task newlinescheduling is implemented for both dependent and independent tasks newline newline