Science

Unveiling the Astonishing World of Quantum Locking

Have you ever heard of an object defying gravity and levitating above a magnetic surface without any visible means of support? Welcome to the mind-bending phenomenon known as “quantum locking.” 🪄🔮 Quantum locking, also referred to as the “Meissner effect,” is a captivating manifestation of quantum physics that seems like...

Comparison of Optimisation Methods for The Knapsack Problem: Steepest Ascent Hill Climbing vs. Simulated Annealing

I have published my article “Comparison of Optimisation Methods for The Knapsack Problem: Steepest Ascent Hill Climbing vs. Simulated Annealing” on ResearchGate.

The paper explores the application of optimization algorithms, specifically Hill Climbing and Simulated Annealing, to solve the knapsack problem. The knapsack problem is a well-known combinatorial optimization problem with various real-world applications. The aim is to select a subset of items with maximum value while adhering to a weight constraint. The paper provides a comparative analysis of the two algorithms, evaluates their performance based on metrics such as best solution, average solution, and iterations required to converge, and identifies the optimal solution. Additionally, it includes a literature review discussing the application areas of the knapsack problem and concludes with the limitations of the chosen search methods and potential avenues for further development.

Exploring AI Optimizers: Enhancing Model Performance and Efficiency

Introduction Artificial Intelligence (AI) optimizers play a crucial role in training and fine-tuning machine learning models. These optimization algorithms are designed to improve model performance, enhance convergence, and efficiently use computational resources. In this article, we will delve into the world of AI optimizers, exploring their importance, popular types, and...