The QoS-driven Service Selection (QSS) problem is a wellknown NP-hard problem in the combinatorial optimization field. Although the QSS problem is naturally multi-objective optimization problem, most of the existing approaches solve the problem in single-objective optimization context. In the recent years, there have been some efforts to tackle the problem in multi-objective optimization contex...

The purpose of this paper is to develop a new two-stage method for fuzzy multi-objective linear program and apply to engineering project portfolio selection. In the fuzzy multi-objective linear program, all the objective coefficients, technological coefficients and resources are trapezoidal fuzzy numbers (TrFNs). An order relationship for TrFNs is introduced by using the interval expectation of...

Most of the research on job shop scheduling problem are concerned with minimization of a single objective. However, the real world applications of job shop scheduling problems are involved in optimizing multiple objectives. Therefore, in recent years ant colony optimization algorithms have been proposed to solve job shop scheduling problems with multiple objectives. In this paper, some recent m...

Pareto optimization, that includes the simultaneous optimization of multiple conflict objectives, has been employed as a high level strategy to reduce the effect of local optima (Segura et al. 2013). This approach was first introduced in (Louis and Rawlins 1993), and later reinvestigated and termed as multi-objectivization in (Knowles, Watson, and Corne 2001). Since then it has been studied by ...

This dissertation presents multi-objective multi-task learning, a new learning framework. Given a fixed sequence of tasks, the learned hypothesis space must minimize multiple objectives. Since these objectives are often in conflict, we cannot find a single best solution, so we analyze a set of solutions. We first propose and analyze a new learning principle, empirically efficient learning. From...