摘要：The gravitational search algorithm (GSA) is
a population-based heuristic optimization technique and has been proposed for
solving continuous optimization problems. The GSA tries to obtain optimum or
near optimum solution for the optimization problems by using interaction in all
agents or masses in the population. This paper proposes and analyzes
fitness-based proportional (rou- lette-wheel), tournament, rank-based and
random selection mechanisms for choosing agents which they act masses in the
GSA. The proposed methods are applied to solve 23 numerical benchmark functions,
and obtained results are compared with the basic GSA algorithm. Experimental
results show that the proposed methods are better than the basic GSA in terms
of solution quality.
