Abstract: A method, apparatus and product for automated multi-objective solution selection. The method comprises obtaining a set of solutions to a multi-objective problem, wherein each solution is a non-dominated solution. The method further comprises computing outranking weights for each pair of solutions and selecting a subset of the set of solutions based upon a defensive criteria in with respect to the outranking weights, wherein the defensive criteria relates to a number of solutions that outrank the subset. The method further comprises outputting the subset of the set of solutions.

Abstract: A method of selecting a group from a plurality of multi objective designs which comply with a plurality of objectives. The method comprises providing a plurality of multi objective designs, each the multi objective design having a plurality of multi objective design objective values which comply with at least one constraint of a Pareto Frontier of an objective space of a plurality of objectives, selecting a group from the plurality of multi objective designs, each member of the group is selected according to a match between at least one objective of respective the plurality of objectives and at least one of a respective gain threshold and a respective loss threshold, and outputting the group.

Abstract: A method comprising using at least one hardware processor for: receiving a multi-objective optimization problem; projecting a Pareto frontier of candidate solutions for said multi-objective optimization problem to a hyperplane; decomposing said hyperplane into multiple Voronoi regions each associated with a candidate solution of said candidate solutions; determining a robustness degree for each candidate solution of said candidate solutions, by computing a hypervolume for each region of said multiple Voronoi regions; and ranking said candidate solutions based on the robustness degree.

Abstract: A method of selecting a group from a plurality of multi objective designs which comply with a plurality of objectives. The method comprises providing a plurality of multi objective designs, each the multi objective design having a plurality of multi objective design objective values which comply with at least one constraint of a Pareto Frontier of an objective space of a plurality of objectives, selecting a group from the plurality of multi objective designs, each member of the group is selected according to a match between at least one objective of respective the plurality of objectives and at least one of a respective gain threshold and a respective loss threshold, and outputting the group.

Abstract: A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with at least one predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior.

Abstract: A method, apparatus and product for automated multi-objective solution selection. The method comprises obtaining a set of solutions to a multi-objective problem, wherein each solution is a non-dominated solution. The method further comprises computing outranking weights for each pair of solutions and selecting a subset of the set of solutions based upon a defensive criteria in with respect to the outranking weights, wherein the defensive criteria relates to a number of solutions that outrank the subset.

Abstract: A method comprising using at least one hardware processor for: receiving a multi-objective optimization problem; projecting a Pareto frontier of candidate solutions for said multi-objective optimization problem to a hyperplane; decomposing said hyperplane into multiple Voronoi regions each associated with a candidate solution of said candidate solutions; determining a robustness degree for each candidate solution of said candidate solutions, by computing a hypervolume for each region of said multiple Voronoi regions; and ranking said candidate solutions based on the robustness degree.

Abstract: A method of visualizing a plurality of designs which comply with a plurality of objectives. The method comprises acquiring a plurality of designs each represented by sequential multivariate data indicative of a compliance with a plurality of objectives, generating an objective anchored based self-organizing map (SOM) having a plurality of objective anchors and maps the plurality of designs in an objective space, and outputting the objective anchored based SOM. Each objective anchor is associated with one of the plurality of objectives, each the design is visualized in the objective anchored based SOM by an indicator which the distance thereof from each the objective anchors is indicative of a compliance thereof with a respective the associated objective in relation to other of the plurality of objectives.

Abstract: A computerized method of providing a multiobjective optimal design through user interactive navigation, comprising: 1) Designating a user reference design which defines multiple objectives in a design space. 2) Exploring the design space to identify a multiobjective optimal design, evolved from the reference design, through multiple navigation iterations. During each iteration the user is interacted to reach an intermediate candidate design which is closer to a Pareto frontier. Each iteration comprising: (a) Identifying and presenting the user, optimal designs which are closer to the Pareto frontier and are within a pre-defined evolution distance from an intermediate design of previous iteration, improving one or more of the objectives. (b) Selecting a preferred design from those candidate designs, according to user instructions, the preferred design is used as the starting point for the next iteration.

Abstract: A method of selecting a group from a plurality of multi objective designs which comply with a plurality of objectives. The method comprises providing a plurality of multi objective designs, each the multi objective design having a plurality of multi objective design objective values which comply with at least one constraint of a Pareto Frontier of an objective space of a plurality of objectives, selecting a group from the plurality of multi objective designs, each member of the group is selected according to a match between at least one objective of respective the plurality of objectives and at least one of a respective gain threshold and a respective loss threshold, and outputting the group.

Abstract: A method of visualizing a plurality of designs which comply with a plurality of objectives. The method comprises acquiring a plurality of designs each represented by sequential multivariate data indicative of a compliance with a plurality of objectives, generating an objective anchored based self-organizing map (SOM) having a plurality of objective anchors and maps the plurality of designs in an objective space, and outputting the objective anchored based SOM. Each objective anchor is associated with one of the plurality of objectives, each the design is visualized in the objective anchored based SOM by an indicator which the distance thereof from each the objective anchors is indicative of a compliance thereof with a respective the associated objective in relation to other of the plurality of objectives.

Abstract: Improved systems and techniques for representation of information. As a user creates a graphic design, properties represented by graphical elements of the design, are associated with visual characteristics of the elements, and a palette of elements is made available for use in development of the design or creation of new designs. As generalized elements are added to a design, the generalized elements may take on visual characteristics associated with properties identified by the positioning of the generalized elements in the design. Ongoing or existing designs may be surveyed to identify elements, properties represented by the elements, and visual characteristics associated with the properties, and new elements added to the palette or existing elements modified as a result of one or more surveys.

Abstract: A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with at least one predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior.

Abstract: A method of determining whether a given diagram is a conceptual model is provided. The method may include the following steps: obtaining a plurality of artifacts, wherein each one of the artifacts exhibits at least one diagram, and wherein at least some of the artifacts exhibit text associated with the diagrams; determining for each diagram, a plurality of specified factors; and estimating, for each diagram, a likelihood of the diagram being a conceptual model based at least partially on the determined factors. Optionally, the method may further include the step of applying a scoring function to the determined factors, to yield a score, wherein the estimating is further based on the score.

Abstract: A computer implemented method for social modeling is disclosed. The method includes entering into a modeling environment one or more model elements by one or more users via communication device communicating over a communication network, performing modeling actions on the model elements, by the users according to their roles, determining if one or more of the model elements satisfy a policy, and if so determining that the policy satisfying model elements are finalized. A computer program product and a data processing system are also disclosed.

Abstract: A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with one or more predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior.

Abstract: A modeling tool may be created directed from a drawing. A plurality of components and their relationships are deduced from the drawing and defined as a metamodel. One or more user operations performed while creating the drawing are also determined and used in building the metamodel. The metamodel may be used in a fixed mode to create a model with definition of the metamodel. The metamodel may be also used in a flexible mode to further redefine the metamodel or create a second metamodel.