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

被引：0

作者：

Rahimi, Iman ^{[1
]}

Picard, Theodore ^{[1
]}

Morabito, Andrew ^{[1
]}

Pampalis, Kiriakos ^{[1
]}

Abignano, Aiden ^{[1
]}

Gandomi, Amir H. ^{[1
]}

机构：

[1] Univ Technol Sydney, Fac Engn & IT, Sydney, NSW, Australia

来源：

2022 9TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE, ISCMI | 2022年

关键词：

metaheuristics; open-pit mining; genetic algorithm; simulated annealing;

D O I：

10.1109/ISCMI56532.2022.10068481

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：109 / 112

页数：4

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