Linear discriminant analysis is considered as current techniques in feature extraction so, LDA, by discriminant information which obtains in mapping space, does the classification act. When the classes' distribution is not normal, LDA, to perform classification, will face problem and will resulted the poor performance of criteria in performing the classification act. One of the proposed ways is the use of other measures, such as Chernoff's distance so, by using Chernoff's measure LDA has been spreading to its heterogeneous states and LDA in this state, in addition to use information among the medians, uses the information of the classes' Covariance matrices. By defining scattering matrix , based on Boundary and non-Boundary samples and using these matrices in Chernoff's criteria, the decrease of the classes' overlapping in the mapping space in as result, the rate of classification correctness increases. Using Boundary and non-Boundary samples in scattering matrices causes improvement over the result. In this article, we use a new discovering multi-stage Algorithm to choose Boundary and non-Boundary samples so, the results of the conducted experiments shows promising performance of the proposing method.
In the last three decades, an integrated approach to optimize logistics system is considered as one of the most important aspects of optimizing supply chain management. This approach involves the ties between locations of facility, allocation of suppliers/customers, structure of transportation routes and inventory control. The aim of this paper is to investigate the ordering planning of a supply chain with multi supplier, multi distribution center, multi customer and one perishable raw material. This paper provides a mathematical model taking in consideration the limitation of raw material corruptibility (perishable material) which belongs to the category of NP-hard problems. To solve the proposed model, the Ant Colony Optimization algorithm (ACO) and Particle Swarm Optimization algorithm (PSO) are employed. In order to improve performances of ACO and PSO parameters, a Taguchi experimental design method was applied to set their proper values. Besides, to evaluate the performance of the proposed model, an example of the dairy industry is analyzed by using MATLAB R 2015a. To validate the proposed meta-heuristic algorithms, the results of them were compared with together. The results of the comparison show that ACO is greater than PSO in speed convergence rate and the number of solutions iterations.
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