Physically Inspired Optimization
(2020) FYSK02 20201Mathematical Physics
Department of Physics
 Abstract
 Optimization is the study of finding the best solution for a problem. The applications of this field ranges from finding the best routes for travelling to finding the ground state of physical systems. This thesis looks at gradient based and gradientless optimization algorithms which are applied to two problems: the linear least square and the position of a system of springs. The algorithms used are Sparse LevenbergMarquardt, Particle Swarm Optimization and NelderMead algorithm. The goal of this paper is to benchmark these algorithms on the two problems. The results show that for small problems the gradientless algorithms converge faster than the gradient based algorithm. However for big problems the gradientless algorithms face... (More)
 Optimization is the study of finding the best solution for a problem. The applications of this field ranges from finding the best routes for travelling to finding the ground state of physical systems. This thesis looks at gradient based and gradientless optimization algorithms which are applied to two problems: the linear least square and the position of a system of springs. The algorithms used are Sparse LevenbergMarquardt, Particle Swarm Optimization and NelderMead algorithm. The goal of this paper is to benchmark these algorithms on the two problems. The results show that for small problems the gradientless algorithms converge faster than the gradient based algorithm. However for big problems the gradientless algorithms face converging issues. Finally, more research into implementation of gradientless algorithms is suggested. (Less)
 Popular Abstract
 Optimization is the study of finding the best solutions for a given problem. The applications of this field ranges from finding the best routes for travelling between different locations to maximizing output of products in a factory. In this thesis two families of algorithms are benchmarked: ones that use mathematical tools and ones that mimic behaviour of biological systems (physically inspired). The results show that physically inspired algorithms have the potential to be more efficient than the algorithms that use mathematical tools however more research needs to be done.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/9022116
 author
 D Costa, EmmanuelRoosevelt ^{LU}
 supervisor

 Andrea Idini ^{LU}
 organization
 course
 FYSK02 20201
 year
 2020
 type
 M2  Bachelor Degree
 subject
 keywords
 Optimization, Particle Swarm Optimization, PSO, LevenbergMarquardt algorithm, Gradient descent algorithm, GaussNewton algorithm, Linear Least Squares, System of Springs
 language
 English
 id
 9022116
 date added to LUP
 20200713 15:24:48
 date last changed
 20200713 15:24:48
@misc{9022116, abstract = {Optimization is the study of finding the best solution for a problem. The applications of this field ranges from finding the best routes for travelling to finding the ground state of physical systems. This thesis looks at gradient based and gradientless optimization algorithms which are applied to two problems: the linear least square and the position of a system of springs. The algorithms used are Sparse LevenbergMarquardt, Particle Swarm Optimization and NelderMead algorithm. The goal of this paper is to benchmark these algorithms on the two problems. The results show that for small problems the gradientless algorithms converge faster than the gradient based algorithm. However for big problems the gradientless algorithms face converging issues. Finally, more research into implementation of gradientless algorithms is suggested.}, author = {D Costa, EmmanuelRoosevelt}, keyword = {Optimization,Particle Swarm Optimization,PSO,LevenbergMarquardt algorithm,Gradient descent algorithm,GaussNewton algorithm,Linear Least Squares,System of Springs}, language = {eng}, note = {Student Paper}, title = {Physically Inspired Optimization}, year = {2020}, }