REGULATION OF THE RELATIONSHIP BETWEEN ADDITIVE REDUCTION AND METRICS METHODS

Objectives. The aim of the work is to determine the relationship between generalised criterion and target programming methods.Methods. The paper considers the aggregation operation that underlies many decision-making procedures used in input-output models, in neural network technologies and in the study of multi-purpose systems. The use of certain metrics within the framework of target programming can lead to solutions that are not Pareto-optimal. Therefore, in targeted programming, a significant place is given to finding the conditions under which the use of one or another metric obviously leads to Pareto-optimal solutions. The necessary (Carlin's theorem) and sufficient conditions of Pareto-optimality are known to perform the additive reduction. For a generalised criterion on the basis of order operators of weighted aggregation, two theorems proven by the author (the theorem on the inclusion of the set of Pareto-optimal solutions into a set of effective solutions and the Pareto optimality theorem for the solution obtained) are presented.Results. The proof of the Pareto optimality theorem of the solution is given, maximising the generalised criterion obtained on the basis of the order operations of weighted aggregation, which justifies the use of operations of this type for solving the problems of vector optimisation or multicriteria choice. The theorem on the existence of an additive reduction for a metric is true only in the particular case and is based on Carlin's theorem, according to which a subset of Pareto-set points maximises some additive reduction.Conclusion. In the paper a relationship between the additive reduction and metrics methods is established. An assertion concerning the relationship between the parameters of the distance function in the target programming method and the weighting coefficients of the additive reduction is formulated and proved, which ensures the equivalence of the optimal Pareto solutions

The induced generalized OWA operator

We present the induced generalized ordered weighted averaging (IGOWA) operator. It is a new aggregation operator that generalizes the OWA operator by using the main characteristics of two well known aggregation operators: the generalized OWA and the induced OWA operator. Then, this operator uses generalized means and order inducing variables in the reordering process. With this formulation, we get a wide range of aggregation operators that include all the particular cases of the IOWA and the GOWA operator, and a lot of other cases such as the induced ordered weighted geometric (IOWG) operator and the induced ordered weighted quadratic averaging (IOWQA) operator. We further generalize the IGOWA operator by using quasi-arithmetic means. The result is the Quasi-IOWA operator. Finally, we also develop a numerical example of the new approach in a financial decision making problem

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered