A Modified Game Theory Approach to Multiobjective Optimization

Many mechanical and structural design problems encountered in practice require solutions which balance several conflicting objectives. The vector, scalarization, and trade-off-curve methods have been developed to achieve multiobjective solutions. One of the best known methods for generating a compromise solution, based on the concept of Pareto minimum solution, is the cooperative game theory method since it uses a scalarized approach and has a numerical measure of compromise. However, game theory is hard to automate due to a two step optimization process involved. Hence, in this work, a modification to the game theory is introduced in which the two optimization steps are combined and an algorithm for its implementation is developed. The algorithm is tested on two numerical examples, including one dealing with the probabilistic design of an eighteen speed machine tool gear train. The probabilistic theory necessary for the design of the gear train is also introduced. The examples validate the modified game theory.