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The use of a unified parametrized scheme for metaheuristics facilitates the development of metaheuristics and their application. The unified scheme can also be used to implement hyperheuristics on top of parametrized metaheuristics, selecting appropriate values for the metaheuristic parameters, and consequently the metaheuristic itself. The applicability of hyperheuristics to efficiently solve computational search problems is tested with the application of local and global search methods (GRASP, Tabu Search, Genetic algorithms and Scatter Search) and their combinations to three problems: a problem of optimization of power consumption in operation of wells, the determination of the kinetic constants of a chemical reaction and the maximum diversity problem. The hyperheuristic approach provides satisfactory values for the metaheuristic parameters and consequently satisfactory metaheuristics.

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Virtual screening (VS) methods aid clinical research by predicting the interaction of ligands with pharmacological targets. The computational requirements of VS, along with the size of the databases, propitiate the use of high-performance computing. METADOCK is a tool for the application of metaheuristics to VS in heterogeneous clusters of computers based on central processing unit (CPU) and graphics processing unit (GPU). HYPERDOCK represents a step forward; the exploration for satisfactory metaheuristics is systematically approached by means of hyperheuristics working on top of the metaheuristic schema of METADOCK. Multiple metaheuristics are explored, so the process is computationally demanding. HYPERDOCK exploits the parallelism of METADOCK and includes parallelism at its own level. The different levels of parallelism can be used to exploit the parallelism offered by computational systems composed of multicore CPU + multi-GPUs. The efficient exploitation of these systems enables HYPERDOCK to improve ligand–receptor binding.