Abstract:
This dissertation focuses on a meta-heuristic optimization algorithm i.e. big bang
big crunch optimization (BBBC) algorithm and major setbacks in BBBC algorithm
with respect to its conceptual and working structure. A modi ed BBBC optimization
algorithm is proposed, which works better than original BBBC. But, it is observed
that BBBC and modi ed BBBC like many other meta-heuristic optimization algorithm
su ers from the problem of getting trapped in local minima. Therefore, modi ed BBBC
is combined with chaos which e ectively enhances the searching e ciency and greatly
improves the searching quality. These algorithms validity is quanti ed using various
benchmark function.
Further, this thesis, contributes various results, techniques and focuses on application
of BBBC in areas of System and Control. Starting with Model Order Reduction
(MOR), which is an integral part of System Engineering. MOR techniques have proved
to be an important technique for accelerating time-domain simulation in a variety of
CAD tools for highly complex system and controller design. There are various reduction
techniques available in literature and most of them are either complex i.e. they are too
di cult to understand while other techniques work for particular class of problems. In
this report, a novel MOR technique has been proposed using BBBC and time moment
matching method, which works for many class of problems. Now, moving onto eld of
Control Engineering, utility of this algorithm for controller design has been elaborated.
In this method, a multi-objective function has been formulated and BBBC is used as
an optimization tool for ne tuning the PID controller. Above work i.e. MOR and
controller design have been validated on automatic voltage regulator system.
Another contribution of this thesis is study of utility of statistical methods in area
of Control System and Optimization. As it is known that BBBC is a relatively new
optimization technique, before which, many famous techniques like PSO and GA are
widely used in all eld of engineering and talked about in optimization society. But
question always raises which one of these algorithms are better in respect to solution
nding capability (e ectiveness) and computational e ciency. In this report we have
used inferential statistics as a tool to analyze this problem and bring out a concrete
conclusion in this regard.
At last we have used Taguchi method, a statistical technique, combined with BBBC to
ne tune the controller parameters.
