GIS Spatial Optimization for Corridor Alignment Using Simulated Annealing

Abstract

Planning corridors for new facilities such as pipeline or transmission lines through geographical spaces is a topographical constraint optimization problem. The corridor planning problem requires finding an optimal route or a set of alternative paths between two locations. This article presents a simulated-annealing-based (SA) approach applying a variable neighborhood strategy in a continuous space to generate competitive and different alternative paths to solve the corridor planning problem. The variable neighborhood method randomly selects two points from a variable interval of the current solution generated by SA creating pseudo-random paths inside a corridor and finding spatially different alternatives. The proposed approach is evaluated with three practical problems using real topographic data from the Veracruz Basin in Mexico. The experimental results show that this approach obtains efficient and competitive solutions with improvements above 18% over those gotten by the compared method.

Transient Storage Model Parameter Optimization Using the Simulated Annealing Method

“…Li et al, 2019;Wang et al, 2019), resource allocation (Aerts & Heuvelink, 2002;X. Li & Ma, 2018), hazard assessment (Hackl et al, 2018;Hosseini et al, 2020), and GIS spatial optimization (Cruz-Chavez et al, 2020). We hypothesize that simulated annealing may be a feasible approach to determine the global minimum parameter vector for hyporheic zone modeling using the TSM.…”

“…It was first used for fitting the parameters for the equation of state for substances consisting of interacting individual molecules (Metropolis et al., 1953). The simulated annealing method has been applied to a large variety of optimization problems, including hydraulic parameter estimation (Rucker, 2011), decision making (Erana‐Diaz et al., 2020; B. Li et al., 2019; Wang et al., 2019), resource allocation (Aerts & Heuvelink, 2002; X. Li & Ma, 2018), hazard assessment (Hackl et al., 2018; Hosseini et al., 2020), and GIS spatial optimization (Cruz‐Chavez et al., 2020). We hypothesize that simulated annealing may be a feasible approach to determine the global minimum parameter vector for hyporheic zone modeling using the TSM.…”

Unifying Variable Importance Scores from Different Machine Learning Models Using Simulated Annealing

“…The positions of the changed entries were randomly chosen. The process continues by replacing w by w' whenever the performance of w' is better than w [12,13]. In this paper, the performance of the solution is Z, the minimum correlation between w and Ri.…”

GIS-Based Simulated Annealing Algorithm for the Optimum Location of Fire Stations in the Madrid Region, Spain: Monitoring the Collapse Index

“…This method has advantages compared to other heuristic methods such as its ability to solve continuous problems [39,40], establishing a strong stochastic neighbourhood search technique compared to the genetic algorithm approach [32,41] In addition to these characteristics, simulated annealing can be integrated in geographic information systems [42] and is more suitable for combinatorial optimisation problems than other types of optimisation techniques using GIS, such as Greenfield analysis. This method does not take into account roads, cities, and peculiarities of geographical areas and cannot address combinatorial optimisation problems [43].…”