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]