An Integrated Design Approach Based On Neural Network And Multi-objective Evolutionary Algorithm In Cigarette Product Parameter Optimization And Its Application

With the development of the cigarette industry, the computer assistance design system of cigarette product obtains more attention by cigarette enterprises than before. This system would establish some effective cigarette models based on history data and experience, and has been taken to instruct the optimization of product design. This thesis mainly studies the parameter optimization module of computer assistance cigarette product design system. Because the cigarette parameter optimization design is an extremely complex black-box multi-objective optimization question, this paper proposed a kind of new optimization method based on a mixed strategy of neural network and multi-objective evolutionary algorithm. Specifically, the main contents and fruits of this thesis are outlined as follows:1,Research on an integrated design approach based on neural network and multi-objective evolutionary algorithm in product parameter optimization.In the real world, many product parameter optimization design questiones are the black-box multi-objective optimization question. The black-box multi-objective optimization question has the character that the system modelling is difficulty and many goals must be optimized coordinate. This thesis integrates the BP neural network and NSGA-II. First, we would make use of artificial neural networks to train historical data and establish a neural network model that can respond the non-linearity mapping relations of the parameter vector space to the goal vector space; next, in the processing of parameter optimization, we would make use of the neural network model to obtain individual fitness.2,Research on a modeling method of artificial neural networks based on the cross connection BP algorithm.The complex system modelling is the key to solve product design parameter optimization question. However, the traditional BP algorithm has some shortcomings, which includes the slow convergence rate, the network-architecture-choosing difficulty, falling into partial minimum easily and so on. This thesis has made some improvement to the traditional BP algorithmfrom in the above three aspects. First, Neural networks with any kind of connections can always be sorted as cross-connected ones. According to traditional multi-layer feed-forward neural network, we elaborated the concept of completely-fully connected neural network and then put forward a cross-connected multi-layer feed-forward neural network algorithm. It can be theoretical proved that the cross-connected neural network can reach ideal results with more concise framework compareing with the non-cross connected neural network.Next, it is difficult for us to choose the neural network structure. On the basis of the network structure equation of multi-layer feed-forward neural network with cross connection, discriminants of quantity of hidden layers and discriminants of quantity of perceptrons each layer are given. According to the discriminants, a new neural network structure optimization algorithm is proposed.Third, a novel approach, combining MOEA with BP, is presented to evolve the neural network. The multi-objective evolution algorithm can work in search space simultaneous by certain scale population, not like gradient method which only deal with one point, thus is helpful in searching the overall optimum point and ensuring the good convergence rate.3,An approach of cigarette process parameter optimization based on neural network and multi-objective evolutionary algorithmAccording to the cigarette craft practice, the parameter optimization model in cigarette product process design is given, and belongs to the black-box multi-objective optimization question. Based on neural network and multi-objective evolutionary algorithm this thesis introduced the integrated design approach to solve this problem, and proposed a approach of cigarette process parameter optimization. This approach was used to ordering-cylinder, and obtained satisfactory effect.4,An approach of cigarette formulation parameter optimization based on neural network and multi-objective evolutionary algorithmThe cigarette formulation parameter optimization is different with the process parameter optimization question for organoleptic character. The appraisal of organoleptic character is an extremely complex process, and it is difficult to get the relation model directly. Also, the organoleptic character belongs to the non-value index; the appraisal result of organoleptic character cannot use in the neural network modeling directly. In view of the above difficulties, this thesis proposed a parameter optimization method in cigarette product design with organoleptic character.First, on the basis of the organoleptic character grading standard in cigarette formulation practice, we could transform the organoleptic character appraisal result into the organoleptic character score, realized the non-value index to the value index transformation. Next, we take tobacco leaf chemical composition as the middle link, separately established the relational model of tobacco proportion with chemical composition and that of chemical composition with organoleptic character score. Then the relational model of tobacco proportion with organoleptic character was given. In this foundation, according to the common parameter optimization method of cigarette product parameter design, we proposed the cigarette formulation parameter optimization method based on neural network and multi-objective evolutionary algorithm. Finally, this approach was used to the formulation design and the formulation maintenance successfully.