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A NEW EXPERIMENTAL STUDY OF GENETIC ALGORITHM AND SIMULATED ANNEALING WITH BOUNDED VARIABLES

Genetic algorithms (GA) can work in very large and complex spaces, which gives them the ability to solve many complex real-world problems. The bounded variables linear programming is formulated as genetic algorithms and simulated annealing (SA). This article demonstrates that genetic algorithms and simulated annealing are much easier to implement for solving network problems compared with constructing mathematical programming formulations, because it is a very simple matter to implement a new cost function and solution constraints when using a GA and SA. Finally, the presented results show that the genetic algorithm and simulated annealing provide a good scheduling methodology to bounded variables programming.