This paper presents a multi-objective wavelet identification procedure for fault detection in dynamic systems. For this purpose, a multi-objective genetic algorithm is used to search for the Pareto frontier. Two objectives are taken into account, the minimization of the residual signal in nominal operating conditions and its maximization in faulty operating conditions. Thus, the proposed approa...

Many practical optimization problems usually have several conflicting objectives. In those multi-objective optimization, no solution optimizing all objective functions simultaneously exists in general. Instead, Pareto optimal solutions, which are “efficient” in terms of all objective functions, are introduced. In general we have many Pareto optimal solutions. Therefore, we need to decide a fina...

Support Vector Machines are models widely used in supervised classification. The classical model minimizes a compromise between the structural risk and empirical risk. In this paper, we consider Machine with feature selection design implement bi-objective evolutionary algorithm for approximating Pareto optimal frontier of two objectives. metaheuristic is based on non-dominated sorting genetic i...

In multidisciplinary optimization a designer solves a problem where there are different criteria usually contradicting each other. In general, the solution of such a problem is not unique. When seeking an optimal design, it is natural to exclude from the consideration any design solution which can be improved without deterioration of any discipline and violation of the constraints; in other wor...

In this study, biobjective mixed-integer linear programming problems are considered and a search-andremove (SR) algorithm is presented. At each stage of the algorithm, Pareto outcomes are searched with the dichotomic search algorithm and these outcomes are excluded from the objective space with the help of Tabu constraints. The algorithm is also enhanced with lower and upper bounds, which are u...

We consider a general framework, where each agent has an outside option of privately known value, that encompasses object allocation, matching with contracts, provision of excludable public goods, and more. First, we show that if a strategyproof mechanism is required to (weakly) Pareto-improve an individually rational and participation-maximal benchmark mechanism, there is at most one choice. A...

Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focu...

The multi-objective optimization problem is the main purpose of generating an optimal set of targets known as Pareto optimal frontier to be provided the ultimate decision-makers. The final selection of point of Pareto frontier is usually made only based on the goals presented in the mathematical model to implement the considered system by the decision-makers. In this paper, a mathematical model...

This paper introduces a method for solving multi-objective optimization problems in uncertain environment. When the uncertainty factors of the optimization problem can be included into the mathematical model, through bounded intervals, [I]RMOA (Interval Robust Multi-objective Algorithm) can find an enclosure of the robust Pareto frontier. In this approach, the robust Pareto solutions are the on...