Spatial Optimization

Spatial optimization focuses on finding the best arrangement of elements within a space, aiming to optimize various objectives like efficiency, cost, or resource allocation. Current research explores diverse approaches, including metaheuristics (e.g., firefighter algorithms), rough set theory incorporating spatial similarity, and the integration of deep learning models (e.g., convolutional neural networks) with spatial optimization for tasks such as itinerary planning and geospatial video analytics. These advancements are improving efficiency and personalization in areas like urban planning, logistics, and resource management, while also addressing computationally challenging large-scale problems.