Search results
Results from the WOW.Com Content Network
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
The problem may be solved by sorting the list and then checking if there are any consecutive equal elements; it may also be solved in linear expected time by a randomized algorithm that inserts each item into a hash table and compares only those elements that are placed in the same hash table cell. [1]
For example, the value model referred to earlier with eight criteria and four categories within each criterion (and 4 8 = 65,536 possible alternatives) has 2,047,516,416 undominated pairs in total (analogous to the nine identified in Figure 2), of which, excluding replicas, 402,100,560 are unique (analogous to the six in the example above). [1 ...
In decision theory, the weighted sum model (WSM), [1] [2] also called weighted linear combination (WLC) [3] or simple additive weighting (SAW), [4] is the best known and simplest multi-criteria decision analysis (MCDA) / multi-criteria decision making method for evaluating a number of alternatives in terms of a number of decision criteria.
Again, under certain conditions the preferences can be represented by a numeric function. Such functions are called cardinal utility functions. The article Von Neumann–Morgenstern utility theorem describes some ways by which they can be calculated. The most general situation is that there are both multiple attributes and uncertainty. For ...
The partial alignments can be tabulated in a matrix, where cell (i,j) contains the cost of the optimal alignment of A[1..i] to B[1..j]. The cost in cell (i,j) can be calculated by adding the cost of the relevant operations to the cost of its neighboring cells, and selecting the optimum.
In a value function model, the classification rules can be expressed as follows: Alternative i is assigned to group c r if and only if + < < where V is a value function (non-decreasing with respect to the criteria) and t 1 > t 2 > ... > t k−1 are thresholds defining the category limits.
In Computers and Intractability [8]: 226 Garey and Johnson list the bin packing problem under the reference [SR1]. They define its decision variant as follows. Instance: Finite set of items, a size () + for each , a positive integer bin capacity , and a positive integer .