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The designs of most digital subscriber lines (DSL) involve the following steps: (a) ASSEMBLE subscribers; (b) OPTIMIZE the subscriber unit parameters to achieve the desired operating distances, and the desired DSL rates; (c) CONFIGURE the access lines (including amplifiers, multiplexers, digital receivers); and (d) CONDUCT preliminary tests and make adjustments, if needed. Normally, the optimization of the subsystems is carried out first. If a good compromise is found in the subsystem, the best parameters are tested to obtain the best overall solution. However, there is no guarantee that the optimum is the global optimum. For any point P in a multi-dimensional space, a local optimum can be a neighbour point with a larger fitness value. In this approach the fitness function is defined to be the least squares of the two vectors of ZY versus Y and UH versus Z, where Y is a vector of transmitter return loss (RTL) measurements and Z is a vector of the DSL line parameters. Li Ying Faerber Ye Xu 2006. The design of a digital subscriber line (DSL) system requires a compromise between the various conflicting objectives of capacity, cost, and performance. Multiobjective optimization is a solution to the functional design problem of a DSL system. In this paper, a method of combined PSO and GA is proposed to carry out the multiobjective optimization design for an ADSL system. The PSO is used for evaluating the fitness of a set of solutions and then performs a quick optimization strategy to obtain a relatively good solution back. The GA is further applied to greedily improve the value of the solution by searching. Simultaneously, the basic principles of the ADSL system are also introduced.

Mathematical programming is well established for the optimization of efficient design plans, process systems, processes, and units of products. These are referred to as programming problems. The optimization problems can be divided into unconstrained and constrained optimization problems. Constrained optimization problems are prone to be extremely difficult to solve. In the 20th century, problems of this type have been generally described by linear, quadratic, mixed integer linear, and nonlinear inequalities. The solution of such problems can be approached by the use of linear programming theory. d2c66b5586