In this paper, we present a practical case of the multiobjective knapsack problem which concerns the elaboration of the optimal action plan in the social and medico-social sector. We provide a description and a formal model of the problem as well as some preliminary computational results. We perform an empirical analysis of the behavior of three metaheuristic approaches: a fast and elitist mult...

In feedback control design, the Bode sensitivity integral dictates that trade-offs must be made frequency-wise. For multiple-in-multiple-out (MIMO) systems, trade-offs can also be made spatially (element-wise). To do this using ‘norm based’ control methods, such as H2 or H∞, one can only change weights on the inputs and the outputs of the systems. This way, it is not possible to exploit all spa...

Generalizing our earlier results on optimization with linguistic variables [3, 6, 7] we introduce a novel statement of fuzzy multiobjective mathematical programming problems and provide a method for findig a fair solution to these problems. Suppose we are given a multiobjective mathematical programming problem in which the functional relationship between the decision variables and the objective...

Recently, a couple of approaches have been developed that combine multiobjective optimization with direct discretization methods to approximate trajectories of optimal control problems, resulting in restricted optimization problems of high dimension. The solution set of a multiobjective optimization problem is called the Pareto set which consists of optimal compromise solutions. A common way to...

Generalizing our earlier results on optimization with linguistic variables [3, 6, 7] we introduce a novel statement of fuzzy multiobjective mathematical programming problems and provide a method for finding a fair solution to these problems. Suppose we are given a multiobjective mathematical programming problem in which the functional relationship between the decision variables and the objectiv...

The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive...

This paper presents the solution of environmental/ economic dispatch (EED) problem by using a multiobjective bees algorithm (MBA) with clustering technique. EED problem is to minimize simultaneously the total fuel cost and environmental pollution of generation while satisfying various constrains. The MBA is modified from Bees Algorithm in order to solve EED problem through compromised fuzzy and...

Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a ’physical’ sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a un...

The paper is concerned with the optimistic formulation of a bilevel optimization problem with multiobjective lower-level problem. Considering the scalarization approach for the multiobjective program, we transform our problem into a scalar-objective optimization problem with inequality constraints by means of the well-known optimal value reformulation. Completely detailed first-order necessary ...

1. Abstract While various multiobjective optimization methods based on metaheuristic techniques have been proposed, these methods still encounter difficulties when handling many variables, or numerous objectives and constraints. This paper proposes a new aggregative gradient-based multiobjective optimization method for obtaining a Pareto-optimal solution set. In this method, the objective funct...