Comparison of Trajectory and Population-Based Algorithms for Optimizing Constrained Open-Pit Mining Problem

Abstract

The problem of open-pit mining optimization is a complex task, often containing many variables. In this paper, we apply a trajectory-based algorithm known as simulated annealing together with a well-known population-based algorithm, genetic algorithm, used to generate solutions for a formulation of the constrained pit problem (CPIT). Three datasets were used to test this simulation, Newman1, zuck_small, and KD. The results show that simulated annealing as a trajectory algorithm possesses a slightly better performance in comparison with the genetic algorithm in terms of profit value.