Optimization of disk replacement as a large domain objective function

There are several motivations for trying to find hardware infrastructure which can be replaced. Most administrators believe in owning a diversified portfolio of disks, tapes to replace older ones, a strategy that has been widely accepted and practiced. When attempting to create an efficient portfolio of disks to be replaced there are numerous factors to consider set of fitness heuristics over a population of disks, the goal here is to find a portfolio that has a high probability of failure and at the same time will ensure savings in power consumption and carbon emission. Given the fundamental financial and price information of power consumed, cooling required, we attempt to use GA (Genetic Algorithm) and a hybrid PSO-EO (Particle Swarm Optimization) algorithm to identify disks that are likely to outperform the existing ones by preventing failure and having excess returns and compare these two approaches.