Land consolidation through parcel exchange among landowners using a distributed Spark-based genetic algorithm

Abstract

AbstractLand consolidation is an essential tool for public administrations to reduce the fragmentation of land ownership. In particular, parcel exchange shows promising potential for restructuring parcel holdings, even more when the number of parcels and owners involved is large. Unfortunately, the number of possible exchange combinations grows very quickly with the number of participating landowners and parcels, with the associated challenge of finding an acceptable solution. In this paper, we present a high-performance solution for parcel exchange based on genetic algorithms. Our proposal, using Apache Spark framework, is based on the exploiting of distributed-memory systems with effortless access in order to reduce the execution time. This also allows increasing the search width through multiple populations that share their advances. This can be achieved without compromising the search depth thanks to the higher amount of resources available from using distributed-memory systems. Our proposal is capable of achieving better solutions in lower amounts of time compared to previous works, showing that genetic algorithms on a high performance system can be used to propose fair parcel exchanges under strict time constraints, even in complex scenarios. The performance achieved allows for fast trial of several options, reducing the time usually needed to perform administrative procedures associated with land fragmentation problems. Specifically, our proposal is capable of combining the benefits of both depth-focused and width-focused multithreaded parallelization. It matches the speedup gains of depth-focused multithreaded parallelization. The width-focused parallelization provides local minimum resilience and fitness value reduction potential. In this paper, multithreading solutions and Spark-based solutions are tested.