Quantum-Inspired Optimization: A Comparative Study of QIHTS, QPSO, and QEA

-

Tags: Quantum-inspired algorithms, QIHTS, QPSO, QEA, metaheuristic optimization, global optimization techniques, Python optimization algorithms, computational intelligence, evolutionary algorithms, swarm intelligence, algorithm benchmarking, optimization research project, GitHub optimization project, engineering portfolio project

In the evolving landscape of computational optimization, traditional algorithms are increasingly being pushed to their limits. To address complex, high-dimensional problems, researchers and engineers are turning toward quantum-inspired metaheuristic algorithms—a space where innovation meets performance.

This project presents a comparative analysis of three advanced optimization techniques:

  • Quantum-Inspired Hazel Tree Search (QIHTS)
  • Quantum Particle Swarm Optimization (QPSO)
  • Quantum Evolutionary Algorithm (QEA)

The objective is simple but powerful: evaluate efficiency, convergence behavior, and solution quality across standard benchmark functions.

TL/DR

This project presents a comparative analysis of advanced quantum-inspired optimization algorithms, including QIHTS, QPSO, and QEA. It focuses on evaluating their performance across benchmark functions, analyzing convergence speed, stability, and solution accuracy. The work demonstrates strong foundations in algorithm design, computational intelligence, and scientific programming using Python. By bridging theoretical concepts with practical implementation, the project highlights the effectiveness of quantum-inspired techniques in solving complex optimization problems. It reflects a deep interest in high-performance computing and scalable solutions, making it relevant for applications in AI, cloud systems, and operations research. This repository showcases structured experimentation, analytical thinking, and a strong engineering approach to problem-solving.

Why This Project Matters

Optimization lies at the core of modern computing—from cloud resource allocation to AI model tuning. This project directly addresses:

  • Global optimization challenges
  • Exploration vs exploitation trade-offs
  • Algorithmic efficiency under constraints

By benchmarking multiple quantum-inspired approaches, this work contributes toward identifying scalable and high-performance solutions for real-world problems.

Project Overview

1. Quantum-Inspired Hazel Tree Search (QIHTS)

QIHTS is a novel algorithm inspired by both natural growth processes and quantum behavior principles. It enhances search diversity while maintaining convergence stability.

Key Highlights:

  • Strong balance between exploration and exploitation
  • Improved convergence rate compared to classical HTS
  • Effective handling of multimodal functions

2. Quantum Particle Swarm Optimization (QPSO)

QPSO extends classical PSO by incorporating quantum mechanics concepts, enabling particles to explore a probabilistic search space rather than deterministic trajectories.

Key Highlights:

  • Eliminates velocity component limitations
  • Higher probability of escaping local minima
  • Efficient for continuous optimization problems

3. Quantum Evolutionary Algorithm (QEA)

QEA leverages quantum bits (qubits) and probabilistic representation to evolve solutions over generations.

Key Highlights:

  • Compact representation of solution space
  • Strong global search capability
  • Suitable for combinatorial optimization

Methodology

The project follows a structured experimental pipeline:

  • Selection of standard benchmark functions
  • Uniform parameter tuning across all algorithms
  • Performance evaluation based on:
    • Convergence speed
    • Accuracy of optimal solutions
    • Stability across iterations

The results are visualized and compared to derive meaningful insights into algorithm performance.

Key Findings

  • QIHTS demonstrates superior convergence stability, especially in complex landscapes
  • QPSO excels in exploration, reducing premature convergence
  • QEA shows robustness in diverse search spaces, though sometimes slower

Overall, the study highlights how hybrid and quantum-inspired approaches outperform traditional methods in specific scenarios.

Academic & Technical Positioning

This work aligns closely with domains such as:

  • Artificial Intelligence & Machine Learning Optimization
  • Operations Research
  • Computational Intelligence

Exploring the Project

For a deeper look into the implementation and results:
👉 https://github.com/anupddas/comparative-analysis-of-qhts-qpso-qea.git

Future Scope

  • Integration with real-world optimization problems (cloud scheduling, logistics, AI tuning)
  • Hybridization with machine learning models
  • Performance scaling using distributed systems (AWS, parallel computing)
Anup Das
As, India
Location: Guwahati, Assam 781021, India

Comments

Post a Comment

Popular posts from this blog

Secure AWS VPC Setup with Bastion Host (Step-by-Step Guide for Beginners) | 2026

-
Image
Learn how to build a secure AWS VPC with Bastion Host step-by-step. Beginner-friendly AWS networking project with real-world architecture and security best practices. Tags: Learn how to build a secure AWS VPC with Bastion Host step-by-step. Hands-on guide with real-world architecture, security best practices, and AWS tips. Introduction In modern cloud environments, security and controlled access are non-negotiable. One of the most widely used patterns in production AWS architectures is the bastion host (jump server) setup — a secure gateway that enables access to private resources without exposing them to the internet. A secure AWS VPC with a Bastion Host allows controlled SSH access to private instances without exposing them to the internet. In this hands-on guide, you will build a production-style AWS network architecture used by real companies to secure cloud environments. If you're preparing for AWS Cloud roles , this project demonstrates strong skills in networking, securit...
Read more

How AWS VPC Works: A Deep-Dive Guide to Virtual Private Cloud (Architecture, Security & Best Practices)

-
Image
A complete, in-depth guide to AWS VPC covering architecture, subnets, routing, security, and real-world design patterns. Learn how to build secure, scalable cloud networks. Introduction Amazon Virtual Private Cloud (VPC) is the foundation of networking in Amazon Web Services. It allows you to design a logically isolated network in the cloud where you control IP addressing, routing, and security. If you are aiming for cloud engineering roles, understanding VPC deeply is non-negotiable. This guide goes beyond basics and explains how VPC actually works in real-world architectures. What is a VPC? A Virtual Private Cloud (VPC) is a private, isolated section of the AWS cloud where you can launch resources like EC2 instances, databases, and load balancers. Think of it as: Your own data center network But fully virtual And controlled via software Key characteristics: Fully customizable IP range (CIDR) Logical isolation from other networks Integrated security layers High scalability Core Compon...
Read more