A hyperheuristic optimization technique
to reduce computational
times for the design of pipeline networks is presented. The proposed
strategy is an A-team approach comprising the guided execution of
three metaheuristics: a genetic algorithm, simulated annealing, and
an ant colony optimization. Besides, a specialized learning mechanism
for information exchange was defined in order to speed up the search
process. Moreover, the algorithm was implemented in parallel so as
to allow several metaheuristics to run simultaneously, thus achieving
a significant reduction of time overhead. In the algorithmic design,
realistic scenarios were employed so as to appraise the impact of
each agent on optimization efficiency. The cases correspond to real-world
offshore infrastructures to be located in the Argentinian marine platform.
They were also analyzed to illustrate the validity and suitability
of the proposed approach. This optimization technique proved to be
competitive since it is able to explore a wide search space fast,
yielding satisfactory solutions.