相比于单目标优化问题,多目标优化问题[1]是系统性地同时优化一系列目标函数的过程,也被称为矢量优化(vector optimization)。 本文将首先介绍单目标优化问题,然后介绍多目标优化问题的基本形式和其基于性质的求解方法~仅供大家参考~ 1. 单目标优化问题 许多无线资源分配问题可以被建模为约束优化问题,一般问题建模可表示...
相比于单目标优化问题,多目标优化问题[1]是系统性地同时优化一系列目标函数的过程,也被称为矢量优化(vector optimization)。 前一篇文章介绍了多目标优化问题的基本形式和基于性质的帕累托最优解确定,这篇文章将继续根据文献[1]总结多目标优化问题的求解方法,并最后给出一个具体例子详细说明求解多目标优化问题的过程...
Common approaches for multiobjective optimization include: Goal attainment:reduces the values of a linear or nonlinear vector function to attain the goal values given in a goal vector. The relative importance of the goals is indicated using a weight vector. Goal attainment problems may also be subj...
Engineering OptimizationMarler, R. T., and Arora, J. S. (2005, in press), "Transformation Methods for Multi-objective Optimization", Engineering Optimization.Marler, R.T. and Arora, J.S., "Function-transformation methods for multi-objective optimization", Engineering optimization, vol.37 (6)...
In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the ...
2.2 Multi-objective Optimization and Pareto-optimal Solutions A basic single-objective optimization problem can be formulated as follows: min f (x) x ∈ S, where f is a scalar function and S is the (implicit) set of constraints that can be de?ned as S = {x ∈ Rm : h(x) = 0, ...
Traditionally, multi-objective optimization problems are usually transformed into single-objective optimization problems using the weighted sum of all the objective functions as a target function. However, the balance between the different objective functions must be controlled manually, which can introduce ...
Solve multiobjective optimization problems in serial or parallel Solve problems that have multiple objectives by the goal attainment method. For this method, you choose a goal for each objective, and the solver attempts to find a point that satisfies all goals simultaneously, or has relatively equal...
optimization have received increasing attention in the re- cent years. Generally, multi-objective local search can be divided into three categories. First, multi-objective search is carried out by heuristic local search methods, such as sim- ...
In some of these works, the optimization is performed considering only one fitness function, and usually is the minimization of the error or by using aggregating functions [31], [40]. However, in other works artificial neural networks are optimized using a multi-objective approach, as in [4]...