Halfway Escape Optimization: A Quantum-Inspired Solution for General Optimization Problems

TL;DR

The paper introduces the Halfway Escape Optimization (HEO), a quantum-inspired metaheuristic that outperforms several existing algorithms on benchmark and real-world optimization problems, demonstrating high effectiveness and adaptability.

Contribution

It presents the novel HEO algorithm inspired by quantum effects, with comprehensive evaluation showing its superiority over traditional methods.

Findings

HEO outperforms PSO, GA, AFSA, GWO, and QPSO on benchmark functions.

HEO demonstrates effectiveness in real-world design and classification tasks.

HEO shows high accuracy in rice classification.

Abstract

This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a quantum-inspired metaheuristic designed to address general optimization problems. The HEO mimics the effects between quantum such as tunneling, entanglement. After the introduction to the HEO mechansims, the study presents a comprehensive evaluation of HEO's performance against extensively-used optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (AFSA), Grey Wolf Optimizer (GWO), and Quantum behaved Particle Swarm Optimization (QPSO). The primary analysis encompasses 14 benchmark functions with dimension 30, demonstrating HEO's effectiveness and adaptability in navigating general optimization problems. The test of HEO in Pressure Vessel Design and Tubular Column Design also infers its feasibility and potential in real-time applications. Further validation of HEO in Osmancik-97 and Cammeo Rice Classification achieves a higher accuracy record.