This paper deals with the problem of finding convex bulges on the Pareto-front of a multi-objective optimization problem. The point of maximum bulge is of particular interest as this point shows good tradeoff properties and it is also close to the non-attainable utopia point. Our approach is to use a population based algorithm to simultaneously promote convex bulges and improve the current appr...

Most of supply chain and Hub location problems involve several conflicting objectives hence requiring a multi-objective formulation. Normally, Multi-objective approaches lead to the maximization of a weighted sum of score functions. Since normalizing these functions and quantifying the weights is not a straightforward process, such approaches are poor in practice. In this research, this difficu...

Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. In contrast to a plethora of recently published articles that formulate the design as the optimization of a single cost function formed by combining distinct and often conflicting design-objectives into a weighted sum, in this work, we take a Multi-objective Optimization (MO) appr...

While injecting weight noise during training has been proposed for more than a decade to improve the convergence, generalization and fault tolerance of a neural network, not much theoretical work has been done to its convergence proof and the objective function that it is minimizing. By applying the Gladyshev Theorem, it is shown that the convergence of injecting weight noise during training an...

We introduce and study a family of models for multi-expert multi-objective/criteria decision making. These models use a concept of weight robustness to generate a risk averse decision. In particular, the multi-expert multi-criteria robust weighted sum approach (McRow) introduced in this paper identifies a (robust) Pareto optimum decision that minimizes the worst case weighted sum of objectives ...

When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropriate search directions for genetic search. In our MOGA, a weight value for each objective in a scalar fitness function was randomly specified. Based on the fitness function with the randomly specified weight values, a ...

BACKGROUND Obesity has become epidemic worldwide and 31.0% of Korean adults are obese. Obesity is the main cause of chronic diseases, such as diabetes, hypertension, cardiac disease, and cancer. The purpose of the study was to examine obesity, body image, depression, and weight-control behaviour among Korean female university students and investigate the differences in body image, depression, a...

Although many analytical works have been done to investigate the change of prediction error of a trained NN if its weights are injected by noise, seldom of them has truly investigated on the dynamical properties (such as objective functions and convergence behavior) of injecting weight noise during training. In this paper, four different online weight noise injection training algorithms for mul...