Frontline solver optimization problem types

FRONTLINE SOLVER OPTIMIZATION PROBLEM TYPES SERIES

The population is then subject to selection that tends to eliminate the worst candidate solutions and keep the best ones. From existing candidate solutions, they generate new solutions through either random mutation of single points or crossover or recombination of two or more existing points. These algorithms maintain a population of candidate solutions, rather than a single best solution so far. Most commercial Solver products are based on evolutionary algorithms.)

FRONTLINE SOLVER OPTIMIZATION PROBLEM TYPES SERIES

(In a genetic algorithm the problem is encoded in a series of bit strings that are manipulated by the algorithm in an 'evolutionary algorithm,' the decision variables and problem functions are used directly. Genetic or Evolutionary Algorithms offer one way to find 'good' solutions to nonsmooth optimization problems. Such methods are nondeterministic or stochastic - they may yield different solutions on different runs, even when started from the same point on the same model, depending on which points are randomly sampled. Hence, most methods rely on some sort of random sampling of possible solutions. In all but the simplest problems, it is impractical to exhaustively enumerate all of the possible solutions and pick the best one, even on a fast computer. In other words, the situation at one possible solution gives very little information about where to look for a better solution. Hence it may not only have multiple feasible regions and multiple locally optimal points within each region - because some of the functions are non-smooth or even discontinuous, derivative or gradient information generally cannot be used to determine the direction in which the function is increasing (or decreasing). Such a problem normally is, or must be assumed to be non-convex. The most difficult type of optimization problem to solve is a nonsmooth problem (NSP).

Frontline solver optimization problem types