【原创】QTOF 数据处理中Pareto Scaling 是什么意思?

Abstract—The notion of Pareto-optimality is one of the major
approaches to multiobjective programming. While it is desirable
to find more Pareto-optimal solutions, it is also desirable to find
the ones scattered uniformly over the Pareto frontier in order to
provide a variety of compromise solutions to the decision maker.
In this paper, we design a genetic algorithm for this purpose. We
compose multiple fitness functions to guide the search, where each
fitness function is equal to a weighted sum of the normalized objective
functions and we apply an experimental design method called
uniform design to select the weights. As a result, the search directions
guided by these fitness functions are scattered uniformly toward
the Pareto frontier in the objective space. With multiple fitness
functions, we design a selection scheme to maintain a good
and diverse population. In addition, we apply the uniform design
to generate a good initial population and design a new crossover
operator for searching the Pareto-optimal solutions. The numerical
results demonstrate that the proposed algorithm can find the
Pareto-optimal solutions scattered uniformly over the Pareto frontier