A template approach to producing incremental objective cost functions for local search meta-heuristics

Meta-heuristic search techniques based on local search operators have proven to be very effective at solving combinatorial optimisation problems. A characteristic of local search operators is that they usually only make a small change to the solution state when applied. As a result, it is often unnecessary to re-evaluate the entire objective function once a transition is made but to use an incremental cost function. For example in the travelling salesman problem, the position of two cities within a tour, may be interchanged. Using an incremental cost function, this equates to an O(1) operation as opposed to an O(n) operation (where n is the number of cities). In this paper, a new approach based on the use of templates is developed for the generic linked list modelling system [4]. It demonstrates that incremental objective cost functions can be automatically generated for given problems using different local search operators.