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How to Search Optimal Solutions in Big Spaces with Networks of Bio-Inspired Processors

  • José Ramón Sánchez CousoEmail author
  • Sandra Gómez Canaval
  • David Batard Lorenzo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

Abstract

Searching for new efficient and exact heuristic optimization algorithms in big search spaces currently remains as an open problem. The search space increases exponentially with the problem size, making impossible to find a solution through a mere blind search. Several heuristic approaches inspired by nature have been adopted as suitable algorithms to solve complex optimization problems in many different areas. Networks of Bio-inspired Processors (NBP) is a formal framework formed of highly parallel and distributed computing models inspired and abstracted by biological evolution. From a theoretical point of view, NBP has been proved broadly to be an efficient solving of NP complete problems. The aim of this paper is to explore the expressive power of NBP to solve hard optimization problems with a big search space, using massively parallel architectures. We use the basic concepts and principles of some metaheuristic approaches to propose an extension of the NBP model, which is able to solve actual problems in the optimization field from a practical point of view.

Keywords

Natural computing Bioinspired computational models Networks of bioinspired processors Optimization metaheuristics Distributed and parallel algorithms 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • José Ramón Sánchez Couso
    • 1
    Email author
  • Sandra Gómez Canaval
    • 1
  • David Batard Lorenzo
    • 2
  1. 1.Department of Computer SystemsUniversidad Politécnica de MadridMadridSpain
  2. 2.Department of Research ManagementUniversity of Informatics SciencesLa HabanaCuba

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