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...

Multiobjective design optimization system of exhaust manifold shapes for a car engine has been developed using Divided Range Multiobjective Genetic Algorithm (DRMOGA) to obtain more engine power as well as to achieve less environmental impact. The three-dimensional manifold shapes are evaluated by the unstructured, unsteady Euler code coupled with the empirical engine cycle simulation code. Thi...

Compliant mechanisms achieve desired force and displacement characteristics through elastic deformation of their structure. Current research in the synthesis of compliant mechanism topology has pursued multiobjective optimization using gradient-based search methods. This paper will explore the use of a randomguided search method for multiobjective optimization of compliant mechanisms through ge...

A large number of techniques, such a neural networks and neurofuzzy systems, are used to produce empirical models based in part or in whole on observed data. A key stage in the modelling process is the selection of features. Irrelevant or noisy features increase the complexity of the modelling problem, may introduce additional costs in gathering unneeded data, and frequently degrade modelling p...

the linear multiobjective transportation problem is a special type of vector minimum problem in which constraints are all equality type and the objectives are conicting in nature. this paper presents an application of fuzzy goal programming to the linear multiobjective transportation problem. in this paper, we use a special type of nonlinear (hyperbolic and exponential) membership functions to ...

A novel adaptive local search method is developed for hybrid evolutionary multiobjective algorithms (EMOA) to improve convergence to the Pareto front in multiobjective optimization. The concepts of local and global effectiveness of a local search operator are suggested for dynamic adjustment of adaptation parameters. Local effectiveness is measured by quantitative comparison of improvements in ...

In binary classification problems, receiver operating characteristic (ROC) graphs are commonly used for visualizing, organizing and selecting classifiers based on their performances. An important issue in the ROC literature is to obtain the ROC convex hull (ROCCH) that covers potentially optima for a given set of classifiers [1]. Maximizing the ROCCH means to maximize the true positive rate (tp...