Adam Stirtan's Blog

Artificial Intelligence continues to reshape our world, and within this vast landscape, optimization algorithms play a crucial role in solving complex problems. Particle Swarm Optimization (PSO) stands out as an elegant and efficient algorithm inspired by the collective behavior of social organisms. In this blog post, we embark on a journey to unravel the significance of PSO and showcase its real-world applications through an interactive web application. Take me the demo:  https://adamstirtan.github.io/pso-js/ Source code: https://github.com/adamstirtan/pso-js/ The Essence of Particle Swarm Optimization Particle Swarm Optimization, introduced by Dr. James Kennedy and Dr. Russell Eberhart in 1995, draws inspiration from the coordinated movement of flocks of birds and schools of fish. It belongs to the family of nature-inspired optimization algorithms and has proven its mettle in solving optimization problems across diverse domains. Navigating the Optimization Landscape Our journey delve

In the realm of pathfinding algorithms, the A* algorithm stands out as a versatile and efficient method for finding the shortest path between two points on a grid. This blog post will delve into the inner workings of the A* algorithm, using JavaScript as the language of choice for our implementation. We'll explore the key components of the algorithm and provide snippets of the source code to illustrate its functionality. Demo https://adamstirtan.github.io/astar-js/ Overview of the A* Algorithm A* (pronounced "A star") is a heuristic search algorithm commonly used in various applications, such as robotics, video games, and geographical information systems. It efficiently finds the optimal path from a start point to an end point while considering the cost of movement and avoiding obstacles. The algorithm maintains two sets: the open set and the closed set. The open set contains nodes to be evaluated, while the closed set contains nodes that have already been evaluated. A* u