Abstract
Spatial optimization seeks optimal allocation or arrangement of spatial units under constraints such as distance, adjacency, contiguity, and pattern. Evolutionary Algorithms (EAs) are well-known optimization heuristics. However, classic EAs, based on a binary problem encoding and bit-operation-based offspring operators, are spatially unaware and do not capture topological and geometric relationships. Unsurprisingly when spatial characteristics are not explicitly considered in the design of EA operators, that EA becomes ineffective because satisfying spatial constraints is computationally expensive. We design and develop novel spatially explicit EA recombination operators, inspired by the path relinking and ejection chain heuristic strategies, that implement crossover and mutation using intelligently guided strategies in a spatially constrained decision space. Our spatial EA approach is general and slots well into the foundational theory of evolutionary algorithms for spatial optimization. We demonstrate improved solution quality and computational performance with a large-scale spatial partitioning application.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.