Performance Evaluation of Dispatching Rules in Job Scheduling by Deep Memory with Particle Swarm Optimization

In this chapter, Deep Memory with Particle Swarm Optimization (DMPSO) algorithm is presented, which is based on Particle Swarm Optimization initialized by the particles of Deep Memory Greedy Search (DMGS). The Particle Swarm Optimization (PSO) is a population based optimization technique, where the population is called a swarm. In PSO, each particle represents a possible solution to the optimization task at hand. In all iteration each particle accelerates in the direction of its own personal best solution found so far, as well as in the direction of the global best position discovered so far by any of the particles in the swarm. This means that if a particle discovers a promising new solution, all the other particles will move closer to it, exploring the region more thoroughly in the process. Here the proposed DMPSO, takes the initial solution from the DMGS solution. The DMPSO schedules using a priority rule in which the priorities are defined by the dispatching rules.