Information Technology Reference
In-Depth Information
1 Introduction
Advancement of technology has led real world problems to become complex and
more challenging. To acquire requisite quality of such advanced technology,
associated problems needed to solve intelligently and ef
ciently. In these days
intelligent techniques become very popular for solving such technology oriented
problems. There are variety of ways to solve a single problem, so it is very crucial
to decide exactly which cases an intelligent technique needs to be adopted. There
are few aspects of such decision. Firstly, the problem in hand has to be feasible for
an intelligent technique. There has to be a plug point in the problem where such
techniques are to be plugged in. Secondly, even if problem is found suitable for
intelligent techniques, the best possible technique has to choose from the archive of
numerous techniques available. Lastly, which of the multiple versions of selected
technique will be most suited for the problem has to be decided.
Before considering any intelligent technique, feasibility analysis of the problem
as well as available techniques have to be carried out. Once suitable technique is
found, arises another key issue in implementation of the technique with respect to
the problem. As far as swarm intelligence techniques are concerned, mostly were
developed for solving optimization problems. Again when we say optimization
problem, it covers huge domain. There are different types of optimization problems
and have special characteristics of each. Very basic notion of optimization is to
nd
best possible solution from a set of solutions (referred as solution space) to any
problem. Corresponding problem with solution space can be summarized with
some functions, generally referred as objective function or
fitness function. Some
problems require constraints along with the functions to de
ne solution space, that
case problem is referred as constrained optimization problem. If problem is de
ned
with linear objective functions and constraints, problem is called linear optimization
problem, otherwise it is termed as nonlinear. Hence, optimization problems can be
categorized as linear or nonlinear on the basis of linearity in the problem de
nition.
Objective functions can be continuous or discrete, accordingly problems are
referred as continuous and combinatorial optimization problem respectively. Most
of the real world problems experience several constraints, sometime those con-
straints are de
ned with nonlinear functions. Often such problems require multiple
objective functions to optimize with necessary constraints, referred as multi-
objective optimization problem. Solution space may have several preferred solu-
tions, each of them represents best solution and cannot be dominated by other. In
this case we have best solution set instead of one best solution, such problems are
referred as multi-modal optimization problems. Determination of possibles best
solution set is very important to engineering problems, but due to constraints
present in the problem, best solutions may not always be realized. Both single and
multi-objective problem experiences such hurdles along with diverse constraints
and linearity. Todays technology oriented problems become more complex with
these issues associated with the problem.
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