Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan

This project explores the optimization of surveillance camera placements using Particle Swarm Optimization (PSO) and Mixed-Integer Linear Programming (MILP). PSO, inspired by the social behaviour of birds flocking or fish schooling, is a heuristic algorithm known for its flexibility and exploration...

Full description

Saved in:

Bibliographic Details
Main Author:	Roslan, `Ain Safia
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/106025/1/106025.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.106025
record_format	uketd_dc
spelling	my-uitm-ir.1060252024-11-30T22:59:31Z Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan 2024 Roslan, `Ain Safia Algorithms This project explores the optimization of surveillance camera placements using Particle Swarm Optimization (PSO) and Mixed-Integer Linear Programming (MILP). PSO, inspired by the social behaviour of birds flocking or fish schooling, is a heuristic algorithm known for its flexibility and exploration capabilities. On the other hand, MILP is a deterministic optimization approach that provides precise solutions through linear programming. The project aimed to find optimal camera placements to minimize the total number of cameras used while maximizing coverage, and to perform a comparative analysis between PSO and MILP. MATLAB was chosen as the primary software due to its robust capabilities in numerical computing and optimization, enabling efficient implementation and analysis of both algorithms. The study applied these optimization techniques to various Binary Integer Programming (BIP) matrix sizes (11×9, 39×24, and 172×49) representing the same 2D layouts, to evaluate their performance in different spatial configurations. The results indicated that both PSO and MILP could achieve high coverage rates, with PSO demonstrating superior flexibility and adaptability in identifying optimal camera placements. 2024 Thesis https://ir.uitm.edu.my/id/eprint/106025/ https://ir.uitm.edu.my/id/eprint/106025/1/106025.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Nor-Al-Din, Siti Musliha
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Nor-Al-Din, Siti Musliha
topic	Algorithms
spellingShingle	Algorithms Roslan, `Ain Safia Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan
description	This project explores the optimization of surveillance camera placements using Particle Swarm Optimization (PSO) and Mixed-Integer Linear Programming (MILP). PSO, inspired by the social behaviour of birds flocking or fish schooling, is a heuristic algorithm known for its flexibility and exploration capabilities. On the other hand, MILP is a deterministic optimization approach that provides precise solutions through linear programming. The project aimed to find optimal camera placements to minimize the total number of cameras used while maximizing coverage, and to perform a comparative analysis between PSO and MILP. MATLAB was chosen as the primary software due to its robust capabilities in numerical computing and optimization, enabling efficient implementation and analysis of both algorithms. The study applied these optimization techniques to various Binary Integer Programming (BIP) matrix sizes (11×9, 39×24, and 172×49) representing the same 2D layouts, to evaluate their performance in different spatial configurations. The results indicated that both PSO and MILP could achieve high coverage rates, with PSO demonstrating superior flexibility and adaptability in identifying optimal camera placements.
format	Thesis
qualification_level	Bachelor degree
author	Roslan, `Ain Safia
author_facet	Roslan, `Ain Safia
author_sort	Roslan, `Ain Safia
title	Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan
title_short	Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan
title_full	Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan
title_fullStr	Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan
title_full_unstemmed	Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan
title_sort	surveillance camera placement optimization using particle swarm optimization (pso) algorithm and mixed-integer linear programming (milp) model / `ain safia roslan
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/106025/1/106025.pdf
_version_	1818588167927758848

Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan

Similar Items