Enhancement of Austero et.al.'s whale optimization algorithm on solving course timetabling problems. 6

By: Christian R. Bangay, Paulyn V. Placido. 4 0 16, [, ] | [, ] |
Contributor(s): 5 6 [] |
Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 4535246Edition: Description: Content type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:
Contents:
Action note: In: Summary: ABSTRACT: The process of crafting a conflict-free course schedule each semester poses a significant challenge, demanding substantial time and effort. Traditional manual timetable creation methods involve extensive labor and personnel, often leading to inefficiencies. Many institutions still rely on manual methods using basic office tools like word processors and spreadsheets. Given the complexity of timetable creation, leveraging expert-derived automated solutions becomes imperative. However, adapting such approaches to diverse problems with varying characteristics and constraints proves challenging. Mirjalili and Lewis (2016) introduced the Whale Optimization Algorithm (WOA), a novel swarm-based optimization technique inspired by humpback whale hunting behaviour. WOA has gained traction in engineering applications due to its versatility across disciplines. Austero et al. explored WOA's potential in solving the University Course Timetabling Problem (UCTP) with modifications. Despite its strengths, Enhanced WOA (EWOA) faces limitations such as increased generation time with large datasets (O(nv2) complexity), overlooking fairness and preferences in workload distribution, and scope for enhancing exploration and exploitation phases. This study proposes a refined algorithm addressing EWOA's limitation and creates a better algorithm. The Enhanced Whale Algorithm's performance enhancements led to the development of the Parallelized Enhanced Whale Optimization Algorithm (PEWOA), surpassing EWOA and WOA in efficiency across datasets of any size O(n) or linear time complexity. PEWOA's emphasis on fairness and preference criteria in workload distribution enhances its practical applicability in real-world timetabling scenarios. Through comprehensive evaluations using diverse datasets, PEWOA demonstrates superior performance, showcasing its potential as a robust solution for optimizing course scheduling processes efficiently and effectively. Other editions:
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)

Undergraduate Thesis : (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024. 56

5

ABSTRACT: The process of crafting a conflict-free course schedule each semester poses a significant challenge, demanding substantial time and effort. Traditional manual timetable creation methods involve extensive labor and personnel, often leading to inefficiencies. Many institutions still rely on manual methods using basic office tools like word processors and spreadsheets. Given the complexity of timetable creation, leveraging expert-derived automated solutions becomes imperative. However, adapting such approaches to diverse problems with varying characteristics and constraints proves challenging. Mirjalili and Lewis (2016) introduced the Whale Optimization Algorithm (WOA), a novel swarm-based optimization technique inspired by humpback whale hunting behaviour. WOA has gained traction in engineering applications due to its versatility across disciplines. Austero et al. explored WOA's potential in solving the University Course Timetabling Problem (UCTP) with modifications. Despite its strengths, Enhanced WOA (EWOA) faces limitations such as increased generation time with large datasets (O(nv2) complexity), overlooking fairness and preferences in workload distribution, and scope for enhancing exploration and exploitation phases. This study proposes a refined algorithm addressing EWOA's limitation and creates a better algorithm. The Enhanced Whale Algorithm's performance enhancements led to the development of the Parallelized Enhanced Whale Optimization Algorithm (PEWOA), surpassing EWOA and WOA in efficiency across datasets of any size O(n) or linear time complexity. PEWOA's emphasis on fairness and preference criteria in workload distribution enhances its practical applicability in real-world timetabling scenarios. Through comprehensive evaluations using diverse datasets, PEWOA demonstrates superior performance, showcasing its potential as a robust solution for optimizing course scheduling processes efficiently and effectively.

5

There are no comments for this item.

to post a comment.

© Copyright 2024 Phoenix Library Management System - Pinnacle Technologies, Inc. All Rights Reserved.