The strategy for the Guided Local Search algorithm is to use penalties to encourage a Local Search technique to escape local optima and discover the global optima. A Local Search algorithm is run until it gets stuck in a local optima. The features from the local optima are evaluated and penalized, the results of which are used in an augmented cost function employed by the Local Search procedure. The Local Search is repeated a number of times using the last local optima discovered and the augmented cost function that guides exploration away from solutions with features present in discovered local optima.
The Guided Local Search procedure is independent of the Local Search procedure embedded within it. A suitable domain-specific search procedure should be identified and employed.
The Guided Local Search procedure may need to be executed for thousands to hundreds-of-thousands of iterations, each iteration of which assumes a run of a Local Search algorithm to convergence.
The algorithm was designed for discrete optimization problems where a solution is comprised of independently assessable `features’ such as Combinatorial Optimization, although it has been applied to continuous function optimization modeled as binary strings.
The λ parameter is a scaling factor for feature penalization that must be in the same proportion to the candidate solution costs from the specic problem instance to which the algorithm is being applied. As such, the value for λ must be meaningful when used within the augmented cost function (such as when it is added to a candidate solution cost in minimization and subtracted from a cost in the case of a maximization problem).