QPSO-CD: Quantum-behaved Particle Swarm Optimization Algorithm with Cauchy Distribution

TL;DR

This paper introduces QPSO-CD, a quantum-inspired particle swarm optimization algorithm enhanced with Cauchy distribution and natural selection, demonstrating superior performance and stability on benchmark and engineering problems.

Contribution

The paper presents a novel hybrid quantum PSO variant with Cauchy distribution and natural selection, improving convergence and solution quality over existing methods.

Findings

QPSO-CD outperforms classical PSO and other variants in benchmark tests.

QPSO-CD effectively solves constrained engineering problems.

QPSO-CD has favorable time complexity and stability.

Abstract

Motivated by particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analysed and compared with the classical PSO. It has been proven that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results showed that QPSO associated with Cauchy distribution and natural selection strategy outperforms other variants in the context of stability and convergence.