Multi-objective Decision-making Based On Improved Genetic Algorithm Method Of Research And Applications

As an uncertain stochastic optimal algorithm, GA is applied in kinds of fields in the past 20 years. And because of its independence, global optimization and implicit parallelism, GA is developed and applied by more and more people.Arising from practical problems, Multi-objective Decision-making Problems (MDPS) plays a significant role in economy, military and other high-tech research fields. Although the approaches for solving MDPS have been available for many years, genetic algorithm have been developed to be ideal techniques for solving MDPS. But in practical application, people discover frequently that the genetic algorithm can converge on the local optimization and no longer evolve, or cannot have the descendant that it is capability cannot surpass it is father is in colony. In colony, each individual is extremely similar and appears the premature convergence phenomenon.In this paper, the elementary theory and methods of genetic algorithm and multi-objective decision-making is introduced. Some improvement of GA on the premature convergence is presented. The improved GA which was designed by this article purposefully chooses mating individual and eliminates similar individual based on mating strategies and maintaining population diversity. Then we test the INGA through two typical functions. The test result indicated that the INGA can get rid of premature convergence well and search the optimal value, the searching efficiency is very well.In addition, we take a ERP productive plan of pharmaceutical factory in the Guizhou Province as an example and propose a multi-objective productive plan model, then apply the improved genetic algorithm to solving it which make the pharmaceutical factory getting rid of the former productive plan process by hand-made. In this way, the impersonal and reasonable productive plan can be decided intelligently by favorite of decision-making person.