This paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive val...

Abstract: This paper presents a sensitivity analysis to multiobjective transportation problem. The proposed approach yields to maximum tolerance percentages in multiobjective transportation problem. The weighted sum approach is used to solve the multiobjective transportation problem. The proposed approach allows changing in both the weights and the objective function coefficients and the right ...

This paper studies the constrained multiobjective optimization problem of finding Pareto critical points of vector-valued functions. The proximal point method considered by Bonnel et al. (SIAM J. Optim., 4 (2005), pp. 953-970) is extended to locally Lipschitz functions in the finite dimensional multiobjective setting. To this end, a new approach for convergence analysis of the method is propose...

The attainment function has been proposed as a measure of the statistical performance of stochastic multiobjective optimisers which encompasses both the quality of individual non-dominated solutions in objective space and their spread along the trade-off surface. It has also been related to results from random closed-set theory, and cast as a mean-like, first-order moment measure of the outcome...

In this paper, Antczak’s -approximation approach is used to prove the equivalence between optima of multiobjective programming problems and the -saddle points of the associated -approximated vector optimisation problems. We introduce an -Lagrange function for a constructed -approximated vector optimisation problem and present some modified -saddle point results. Furthermore, we construct an -ap...

In this paper, the parametrically generalized sufficient efficiency conditions for multiobjective fractional programming based on the hybrid (Φ, ρ, η, ζ, θ)−invexities are developed, and then efficient solutions to the multiobjective fractional programming problems are established. Furthermore, the obtained results on sufficient efficiency conditions are generalized to the case of the ε−efficie...

This paper presents a new approach to multiobjective optimization based on the principles of probabilistic uncertainty analysis. At the core of this approach is an efficient nonlinear multiobjective optimization algorithm, Minimizing Number of Single Objective Optimization Problems (MINSOOP), to generate a true representation of the whole Pareto surface. Results show that the computational savi...

This paper presents a new multiobjective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new Strength Pareto Evolutionary Algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable obj...

Real-world problems generally involve several antagonistic objectives, like quality and cost for design problems, or makespan and cost for planning problems. The only approaches to multiobjective AI Planning rely on metrics, that can incorporate several objectives in some linear combinations, and metric sensitive planners, that are able to give different plans for different metrics, and hence t...

controller design and optimization problems, with more than one objective, are referred as multiple objectives or multiple attributed problems. in this paper, a novel method is proposed for designing optimum pid controller that is called genetic multiple attributed decision making method (gmadm). this method is newer than the previous methods and in this paper some options are considered that h...