Simulation optimization of complex systems with noisy
parameter spaces can become computationally expensive
on a single processor system. This book discusses
construction of software for solving optimization
problems by distributing the work load among several
processors residing on a network. Open source
repositories are used for the development of this
software. The Simulated Annealing algorithm is used
to search the parameter space for optimization.
Application of the software to stochastic and
deterministic problem scenarios is closely examined.
Since the convergence of the simulated annealing
algorithm depends on the choice of annealing
parameters, different types of simple and elaborate
cooling schedules are applied to problem instances
and their impact on the quality of convergence is
assessed.
Simulation Optimization Using Simulated Annealing: A Network-based Implementation and Study of CoolingSchedules