METAHEURISTIC OPTIMIZATION METHODS
Metaheuristic optimization methods are the population-based stochastic
search techniques. The population is defined by a set of individuals
which represent potential solutions of the optimization
problem. The number of individuals (N ) is named as the
size of the population. In general, an individual can be represented as
vector whose elements are the values of the control variables of the
optimization problem. The number of control variables (n ) is the
search space dimension of the optimization problem.
The essence of metaheuristic methods is iterative correction of the
solution, ie. generating a new population by applying algorithmic
operators with stochastic search mechanism on individuals from the
current population. The way in which are defined the
algorithmic operators constitutes the essence of a particular
metaheuristic optimization method. The efficiency and performance of
metaheuristic optimization methods are dependent on the proper setting
of the corresponding algorithmic parameters.
General structure of metaheuristic optimization methods can be
represented as follows [3,5]