A Design Framework for Metaheuristics: Problem Types and Avoiding Bottlenecking

This paper is concerned with an aspect of the design of metaheuristic algorithms, such as evolutionary algorithms, tabu search and ant colony optimization. The topic that is considered is how problems can be represented when they are given to a metaheuristic algorithm. A particular difficulty is presented, viz. the ''bottleneck'', where the problem is artificially converted into a new representation in order to fit the standard input to the metaheuristic. Such bottlenecks cause problems in interpreting or trusting the solution given by the metaheuristic. In order to alleviate this problem, we suggest ways in which three types of problem (data-driven, specification-driven and interactive) can be presented to metaheuristics in a bottleneck-free way, and how problems which use multiple solution-types can be tackled