The Pso In The Decision-making Support Multi-objective Static Optimization Algorithm Applied Research Of The Problem

The decision-making problem has existed since ancient times,it can be divided into the single-objective and the multi-objective division.The multi-objective decision refers to getting a number of goals,and these goals have relationships each other,and have conditions each other,too.The viewpoint of system is needed to use in these kinds of decisions,to research and analyze a lot of goals systematically.The decision support,as a newly-developed information technology,can provide enterprises a variety of decision informations,and solutions to many business issues,so it will lighten the manager's burden on the low-level information to deal with and analyse,and if will make the managers focus their decision wisdoms and experiences on their most important work.Thereby,the decision quality and efficiency will be heightened.With the management and operation getting more and more complex in the large-scale industrial enterprises,the policy-makers need a theoretical and systematic aider urgently in policy-decision.Therefore,the combination of computer and information management,decision support system(DSS) came into being.The multi-objective optimization problem is an important part of optimizer in the decision support system.If can make use of the existing research results more efficiently and improve the auxiliary capacity of decision support for the policy-maker.Particle Swarm Optimization (PSO) is applied to support multi-objective decision-making problem,it can make use of existing research results and improve the auxiliary ability of decision support for policy-makers.The PSO was put forward jointly by James Kennedy,an American social Psychologist,and Russell Eberhart,an Electrical engineer in 1995.Their basic ideas were stemmed from the enlightenment of their early researching results in the mass birds action,and also from the existing models of living colony.Since the PSO has fast calculation,simple calculation method,and easy realization. Scholars in various fields paid close attention to it at once as it was put forward.In this dissertation,I started from the basic component of the DSS,focused the discussion on improving the algorithm of the PSO in solving multi-objective optimization problems,and its application in decision support.Taking into account the disadvantage that the elementary particle swarm optimization algorithm falls into local minimum easily,and has low searching accuracy,I set up an algorithm of multi-particle swarm in a restricted area,and applied it to multi-objective optimization algorithm,and put forward the decision support model of multi-objective particle swarm optimization problem.After doing the computer program imitation.I analyzed contrastively the problems about the improved algorithm for treating optimization and about the different algorithms to solve multi-objective decision in decision support.If showed that the algorithm of multi-particle swarm optimization in a restricted area as putting forward in this thesis,and the improved algorithm of multi-objective optimization,are superior to the traditional particle swarm optimization and multi-objective optimization algorithm.So a new way was opened up for studying multi-objective optimization problem in decision support system.