Design And Implementation Of Collaborative Decision-making System Based On Hybrid Genetic Algorithm

Genetic algorithm is a traditional merit-based algorithm, its design idea is basedon natural selection, survival of the fittest approach to retain excellent chromosomedown, removing inferior chromosomes, to ensure that each genetic population canmake the best of the continuity factor in the genetic algorithm there is a certainprobability of mutation, the mutation operation is to ensure the diversity of thesolution space, and try to avoid the local optimal solution space repeatedly search thesolution space, resulting in the phenomenon of local optima.The optimal solution search process simply can not be achieved using geneticalgorithms and maximum efficiency, the algorithm itself has many drawbacks anddisadvantages of genetic algorithm include of: premature, that the algorithmpremature convergence; higher complexity of the algorithm, ie genetic algorithmitself requires a lot of calculation to get optimal results; poor stability, namely geneticoptimization algorithm is carried out by randomness, so the large amount ofcalculation algorithm itself, poor stability optimal results. Genetic algorithm itselfthese shortcomings need to use other algorithms to determine the genetic algorithmoptimization effectively form a new hybrid algorithm for use, for example, theformation of simulated annealing algorithm genetic annealing algorithm, antalgorithms using genetic algorithms ant form, using neural networks BP to optimizethe formation of secondary neural network genetic algorithm, the hybrid geneticalgorithm optimized for both these genetic algorithm optimal set of capabilities, andenhance the performance of genetic algorithms in certain aspects.The collaborative project refers to a specific project requirements, the use ofspecial items and more participants shared project implementation, with thecomplexity of the development of network technology and project development,collaborative project planning needs to be carried out using more and more whilecollaborative project itself has many problems to be solved, such as: resourceallocation, task allocation, resource tracking, error analysis, the results of analysis,these problems are unstructured problems can not be solved by a deterministicalgorithm, only optimization algorithm for the optimal use of the result set optimizingoperation, so you can consider hybrid genetic algorithm to optimize the development process unstructured problem solving collaborative project plan to complete thedevelopment of collaborative project work.The use of hybrid genetic algorithm to solve unstructured problems incollaborative projects, the development of a mixed genetic algorithm based decisionsupport system, developed using J2EE architecture, developed using the Strutsframework assisted in the development process. This article describes the needs of theentire decision-making system analysis, outline design, detailed design. How tointroduce hybrid genetic algorithm implemented in the decision-making system, theissue of how the model will be saved in the decision-making system, how to evaluatethe results of the decisions, the last of the entire system software testing, test thefunctionality and performance of the whole system has reached the user’srequirements.