We study a general multiobjective optimization problem with variational inequality, equality, inequality and abstract constraints. Fritz John type necessary optimality conditions involving Mordukhovich coderivatives are derived. They lead to Kuhn-Tucker type necessary optimality conditions under additional constraint qualifications including the calmness condition, the error bound constraint qu...

This paper presents an approach to adapt the suppression and scaling factor from a single input single output (SISO) dynamic matrix controller (DMC) thought a multiobjective optimization algorithm. To optimize, a nonlinear neural network (NN) process model is used, combined with a multiobjective evolutionary algorithm called SPEA II (Strength Pareto Evolutionary Algorithm) to find better contro...

In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasig...

xi Zusammenfassung xiii Statement of Contributions xv Acknowledgments xvii List of Symbols and Abbreviations xvii  Introduction  . Introductory Example . . . . . . . . . . . . . . . . . . . . . . . .  .. Multiobjective Problems . . . . . . . . . . . . . . . . . . .  .. Selecting the Best Solutions . . . . . . . . . . . . . . . . .  .. The Hypervolume Indicator . . . . . . . . . ...

A newmultiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposedMOPSO technique has been implemented to solve the EED problemwith ...

In this paper, we present an extension of the heuristic called “particle swarm optimization” (PSO) that is able to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle and is based on the idea of having a set of subswarms instead of single particles. In each sub-swarm, a PSO algorithm is executed and, a...

Optimization is undoubtedly playing an ever more important role in CAD of electronic circuits. As the complexity of practical designs grows, so does the number of objectives to be optimized simultaneously. Even though a large number of multiobjective optimization methods have been developed in other disciplines like Operations Research, they are still generally unknown to electrical engineers a...

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...

Report The aim of this work is to make some investigations concerning duality for mul-tiobjective optimization problems. In order to do this we study first the duality for scalar optimization problems by using the conjugacy approach. This allows us to attach three different dual problems to a primal one. We examine the relations between the optimal objective values of the duals and verify, unde...