High-Performance Pareto-Based Optimization Model for Spatial Land Use Allocation

Abstract

Spatial land use allocation is often formulated as a complex multiobjective optimization problem. As effective tools for multiobjective optimization, Pareto-based heuristic optimization algorithms, such as genetic, artificial immune system, particle swarm optimization, and ant colony optimization algorithms, have been introduced to support trade-off analysis and posterior stakeholder involvement in land use decision making. However, these algorithms are extremely time consuming, and minimizing the computational time has become one of the largest challenges in obtaining the Pareto frontier in spatial land use allocation problems. To improve the efficiency of these algorithms and better support multiobjective decision making in land use planning, high-performance Pareto-based optimization algorithms for shared-memory and distributed-memory computing platforms were developed in this study. The OpenMP and Message Passing Interface (MPI) parallel programming technologies were employed to implement the shared-memory and distributed-memory parallel models, respectively, in parallel in the Pareto-based optimization algorithm. Experiments show that both the shared-memory and message-passing parallel models can effectively accelerate multiobjective spatial land use allocation models. The shared-memory model achieves satisfying performance when the number of CPU cores used for computing is less than 8. Conversely, the message-passing model displays better scalability than the shared-memory model when the number of CPU cores used for computing is greater than 8.