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Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields.

Manuel S Alvarez-Alvarado1, Francisco E Alban-Chacón2, Erick A Lamilla-Rubio2

  • 1Faculty of Electrical and Computer Engineering, Escuela Superior Politécnica del Litoral, EC090112, Guayaquil, Ecuador. mansalva@espol.edu.ec.

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Three new quantum-inspired algorithms were developed. The Lorentz potential field algorithm demonstrated superior performance in optimization tasks, balancing accuracy, speed, and efficiency.

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Area of Science:

  • Computational intelligence
  • Quantum mechanics
  • Optimization algorithms

Background:

  • Metaheuristic algorithms are inspired by natural phenomena.
  • Quantum mechanics offers unique properties for computational approaches.
  • Particle Swarm Optimization (PSO) is a widely used metaheuristic.

Purpose of the Study:

  • To introduce three novel quantum-inspired metaheuristic optimization algorithms.
  • To evaluate their performance against established algorithms like PSO, GA, and FFA.
  • To analyze the impact of different quantum potential fields on algorithm efficacy.

Main Methods:

  • Development of three quantum-inspired algorithms using Lorentz, Rosen-Morse, and Coulomb-like square root potentials.
  • Comparative analysis using 24 benchmark functions (unimodal, multimodal, fixed-dimension multimodal).
  • Performance evaluation based on exploitation, exploration, and simulation time.

Main Results:

  • The Lorentz potential field-inspired algorithm exhibited the most balanced performance.
  • This algorithm showed superior exploitation (accuracy, precision) and exploration (convergence speed, acceleration).
  • A strong potential field within a well with weak asymptotic behavior enhances optimization attributes.

Conclusions:

  • Quantum-inspired algorithms offer a promising avenue for metaheuristic optimization.
  • The Lorentz potential field provides an effective framework for balancing exploration and exploitation.
  • The proposed algorithms, particularly the Lorentz-based one, are computationally efficient for complex optimization problems.