Advanced Methods

The research effort leading to the OptQuest Solver Engine is described in the book Tabu Search by Fred Glover and Manuel Laguna, and the book Scatter Search by Manuel Laguna and Rafael Marti. Click here to order it at our online bookstore.

The OptQuest Solver offers several options and tolerances to improve the speed and effectiveness of its metaheuristic methods on various types of problems. You can control several precision settings, and the frequency with which the boundaries of the solution space will be explored. You can set a random number seed to ensure that the same solutions are found on different runs of the OptQuest Solver.  You can also check your problem size and the corresponding Solver engine size limits at any time.

OptQuest Solver Options dialog (30021 bytes)

OptQuest Solver
Options dialog

Intelligent, Population-Based Search

Where classical optimization methods keep track of a single "best solution" found so far, the OptQuest Solver maintains a population of candidate solutions. Any member of the population can give rise to a new, better solution, possibly far away from the "best solution" found so far.  Because of this, the OptQuest Solver is unlikely to become "trapped" in the region of the locally optimal solution.

Tabu Search and Scatter Search  

The OptQuest Solver uses metaheuristics such as tabu search and scatter search, with "memory" and "aging" of members of the population, to guide the generation of new trial solutions. Compared to a genetic or evolutionary algorithm, like the one used in the Evolutionary Solver in the Premium Solver Platform, the OptQuest Solver makes greater use of strategic choices and less use of randomization.

For example, the OptQuest Solver generates new points from linear combinations of existing points in the population, designed to lie both inside and outside the convex region spanned by the existing points -- where an evolutionary algorithm typically generates new points from random or semi-random combinations of existing points.

Automatic Handling of Linear Constraints

While it can handle any type of Excel function -- linear, nonlinear, or non-smooth -- the OptQuest Solver will automatically recognize and exploit any linear constraints in the model. It uses a variant of the Simplex method to "solve for" these constraints, thereby effectively reducing the dimensionality of the space to be explored. Models that include a significant number of linear constraints in addition to some non-smooth constraints are especially well-suited for the OptQuest Solver.

Highly Effective Handling of Integer Constraints

The OptQuest Solver is especially effective at solving non-smooth problems with integer constraints (including "alldifferent" constraints).  It often outperforms all other known solution methods on these problems, including genetic and evolutionary algorithms -- even the Premium Solver Platform's hybrid Evolutionary Solver.

Population Report

The OptQuest Solver Engine can produce a Population Report in addition to the standard Answer Report. This report gives you summary information about the entire population of candidate solutions maintained by the Solver at the end of the solution process. The Population Report can give you insight into the performance of the OptQuest Solver Engine as well as the characteristics of your model, and help you decide whether additional Solver runs are likely to yield even better solutions.

OptQuest Population Report (43949 bytes)

OptQuest Population
Report worksheet

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