The seminar "Hybrid and Robust Approaches to Multiobjective Optimization" was a sequel to two previous Dagstuhl seminars (04461 in 2004 and 06501 in 2006). The main idea of this seminar series has been to bring together two contemporary elds related to multiobjective optimization Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) to discuss critical rese...

This note presents the characterization of the stability set of the first kind for multiobjective nonlinear programming (MONLP) problems with fuzzy parameters either in the constraints or in the objective functions without any differentiability assumptions. These fuzzy parameters are characterized by triangular fuzzy numbers (TFNs). The existing results concerning the parametric space in convex...

This paper proposes a fuzzy-robust stochastic multiobjective programming (FRSMOP) approach, which integrates fuzzy-robust linear programming and stochastic linear programming into a general multiobjective programming framework. A chosen number of noninferior solutions can be generated for reflecting the decision-makers’ preferences and subjectivity. The FRSMOP method can effectively deal with t...

The linear multiobjective transportation problem is a special type of vector minimum problem in which constraints are all equality type and the objectives are conicting in nature. This paper presents an application of fuzzy goal programming to the linear multiobjective transportation problem. In this paper, we use a special type of nonlinear (hyperbolic and exponential) membership functions to ...

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve...

The use of Surrogate Based Optimization (SBO) is widely spread in engineering design to reduce the number of computational expensive simulations. However, “real-world” problems often consist of multiple, conflicting objectives leading to a set of competitive solutions (the Pareto front). The objectives are often aggregated into a single cost function to reduce the computational cost, though a b...

In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strateg...

A hybrid fuzzy knowledge-based system with crisp and fuzzy rules as well as numerical methods was developed for multiobjective optimization of power distribution system operations. The development process and knowledge acquisition process for the fuzzy knowledge-based system are described in detail. The fuzzy knowledge-based system has a four-level rule hierarchy. After a heuristic preprocessor...

This paper presents a fuzzy multi-objective approach for achieving the minimum active power loss and the maximum voltage magnitude in order to improve the efficiency of radial distribution networks with distributed generations. Multi-objective function are considered for load balancing among the feeders, minimization of the real power loss, deviation of nodes voltage, and branch current constra...

This paper presents a congestion management methodology in deregulated power systems by optimal location and sizing of Unified Power Flow Controller (UPFC) in a transmission network. A multiobjective optimization problem has been developed by considering total congestion cost, power loss and system severity. A Fuzzy based approach has been proposed to identify the optimal location and sizing of...