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Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Recently, an evolutionary multiobjective optimization (EMO) approach was proposed, [7] in which a suitable second objective is added to the originally single objective multimodal optimization problem, so that the multiple solutions form a weak pareto-optimal front. Hence, the multimodal optimization problem can be solved for its multiple ...
In the second part, test functions with their respective Pareto fronts for multi-objective optimization problems (MOP) are given. The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [1] Haupt et al. [2] and from Rody Oldenhuis software. [3]
The optimization of portfolios is an example of multi-objective optimization in economics. Since the 1970s, economists have modeled dynamic decisions over time using control theory. [14] For example, dynamic search models are used to study labor-market behavior. [15] A crucial distinction is between deterministic and stochastic models. [16]
Goal programming is a branch of multiobjective optimization, which in turn is a branch of multi-criteria decision analysis (MCDA). It can be thought of as an extension or generalisation of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved.
Such methods are known as ‘numerical optimization’, ‘simulation-based optimization’ [1] or 'simulation-based multi-objective optimization' used when more than one objective is involved. In simulation experiment, the goal is to evaluate the effect of different values of input variables on a system.
This term is misleading because a single efficient point can be already obtained by solving one linear program, such as the linear program with the same feasible set and the objective function being the sum of the objectives of MOLP. [4] More recent references consider outcome set based solution concepts [5] and corresponding algorithms.
Objective function can be to minimize the makespan, the L p norm, tardiness, maximum lateness etc. It can also be multi-objective optimization problem. Jobs may have constraints, for example a job i needs to finish before job j can be started (see workflow). Also, the objective function can be multi-criteria. [4]