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A Quantum Inspired GVNS: Some Preliminary Results.

Christos Papalitsas1, Panayiotis Karakostas2, Kalliopi Kastampolidou3

  • 1Department of Informatics, Ionian University, Corfu, Greece. c14papa@ionio.gr.

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A new quantum-inspired variant of the Great Vertex Neighborhood Search (GVNS) algorithm, called qGVNS, leverages quantum computing for optimization problems. This novel approach enhances the perturbation phase for improved efficiency in solving complex combinatorial problems.

Keywords:
MetaheuristicsOptimizationQuantum inspired algorithmsTSPVNSVariable neighborhood searchqVNS

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Area of Science:

  • Computer Science
  • Operations Research
  • Quantum Computing

Background:

  • The Great Vertex Neighborhood Search (GVNS) is a recognized metaheuristic for NP-Hard Combinatorial Optimization problems.
  • Existing metaheuristics face challenges in efficiently solving complex optimization tasks.

Purpose of the Study:

  • To introduce a novel quantum-inspired variant of GVNS, termed qGVNS.
  • To explore the potential of quantum computing principles in enhancing metaheuristic algorithms.

Main Methods:

  • Developed qGVNS, a variant of GVNS incorporating quantum computing principles in its perturbation phase.
  • Conducted a comparative study of qGVNS against the standard GVNS.
  • Tested algorithm performance on selected TSPLib instances using both first and best improvement strategies.

Main Results:

  • The qGVNS demonstrated competitive or improved efficiency compared to the standard GVNS.
  • Quantum-inspired perturbation effectively enhances the search capabilities of the GVNS framework.
  • Performance was validated across various instances of the Traveling Salesperson Problem (TSP).

Conclusions:

  • qGVNS represents a promising advancement in metaheuristic optimization by integrating quantum principles.
  • The quantum-inspired approach offers a viable strategy for tackling complex NP-Hard problems more efficiently.
  • Further research into quantum-enhanced metaheuristics is warranted.