Proposing effective coordinate search methods for solving large-scale expensive black-box optimization problems
dc.contributor.advisor | Rahnamayan, Shahriyar | |
dc.contributor.author | Rokhsatyazdi, Ehsan | |
dc.date.accessioned | 2021-02-24T16:23:54Z | |
dc.date.accessioned | 2022-03-29T16:49:32Z | |
dc.date.available | 2021-02-24T16:23:54Z | |
dc.date.available | 2022-03-29T16:49:32Z | |
dc.date.issued | 2020-08-01 | |
dc.degree.discipline | Electrical and Computer Engineering | |
dc.degree.level | Master of Applied Science (MASc) | |
dc.description.abstract | In engineering and science, optimization plays a vital role in many real-world applications. In this work, several novel optimization algorithms based on Coordinate Search (CS) algorithm are proposed. CS is a gradient-free technique and we have enhanced them for solving Black-box, non-convex, and expensive large-scale problems. These CS-based algorithms can handle mixed-type variables. When an optimization problem is large-scale and expensive, it is a very challenging problem to solve because it is intersecting two conflicting properties. Large-scale problems require extensive fitness evaluations, but each evaluation is time consuming. It gets more challenging when the budget is limited, which is the case in most real-word applications. The proposed CS-based algorithms reduce the search space exponentially; this makes it a powerful method for optimizing high-dimensional problems with limited budget. The proposed algorithms show a very promising performance on optimizing high-dimensional problems; tested on the CEC-2013 benchmarks problems and neural network training. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.identifier.uri | https://hdl.handle.net/10155/1234 | |
dc.language.iso | en | en |
dc.subject | Coordinate-search | en |
dc.subject | Gradient-free | en |
dc.subject | Non-convex | en |
dc.subject | Neural-network | en |
dc.subject | Large-scale optimization | en |
dc.title | Proposing effective coordinate search methods for solving large-scale expensive black-box optimization problems | en |
dc.type | Thesis | en |
thesis.degree.discipline | Electrical and Computer Engineering | |
thesis.degree.grantor | University of Ontario Institute of Technology | |
thesis.degree.name | Master of Applied Science (MASc) |