An eecient numerical solution scheme entitled adaptive diierential dynamic programming is developed in this paper for multiobjective optimal control problems with a general separable structure. For a multiobjective control problem with a general separable structure, the \optimal" weighting coeecients for various performance indices are time-varying as the system evolves along any noninferior tr...

Multiobjective decision-making and combinatorial optimization have been studied extensively over the past few decades (cf. [16], and [4] for bibliographies). Both fields play a decisive role in multiobjective combinatorial optimization, for which the class of (multiobjective) portfolio selection is of particularly high practical relevance (cf. [10] for a survey). Research and development (R&D) ...

In this paper we address the problem of multiobjective attribute selection in data mining. We propose a multiobjective genetic algorithm (GA) based on the wrapper approach to discover the best subset of attributes for a given classification algorithm, namely C4.5, a well-known decision-tree algorithm. The two objectives to be minimized are the error rate and the size of the tree produced by C4....

This paper presents the Parallel Single Front Genetic Algorithm (PSFGA), a parallel Pareto-based algorithm for multiobjective optimization problems based on an evolutionary procedure. In this procedure, a population of solutions is sorted with respect to the values of the objective functions and partitioned into subpopulations which are distributed among the processors. Each processor applies a...

In this paper, we conduct a fitness landscape analysis for multiobjective combinatorial optimization, based on the local optima of multiobjective NK-landscapes with objective correlation. In singleobjective optimization, it has become clear that local optima have a strong impact on the performance of metaheuristics. Here, we propose an extension to the multiobjective case, based on the Pareto d...

In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

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 this work we consider a multiobjective open shop scheduling problem with uncertain processing times and flexible due dates, both modelled using fuzzy sets. We adopt a goal programming model based on lexicographic multiobjective optimisation of both makespan and due-date satisfaction and propose a particle swarm algorithm to solve the resulting problem. We present experimental results which s...

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 . . . . . . . . . ...