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Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

مجلة بغداد للعلوم, 2021
The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (
Zahid Iqbal, Rafia Ilyas, Huah Yong Chan, Naveed Ahmed   +3 more
doaj   +1 more source

Distributed hyper-heuristics for real parameter optimization [PDF]

Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009
Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance than any of the original heuristics. While HHs lend themselves naturally for distributed deployment, relatively little attention has been paid so far on the design and evaluation
Biazzini, Marco, B. Bánhelyi, Montresor, Alberto, M. Jelasity   +3 more
openaire   +2 more sources

Machine Learning-Driven Tri-Level Hyper-Heuristic Selection With Adaptive Move Acceptance for Composing Medical Crew Scheduling Problem

IEEE Access
Driven by the growing demand for adaptive and automated search control in combinatorial optimization, hyper-heuristic frameworks have become an important approach for handling heterogeneous problem instances.
Alaa Khudhair Abbas, Esam Taha Yassen
doaj   +1 more source

Hyper Heuristic Memetic-Algorithm Based Optimization of Solar Photovoltaic Systems [PDF]

E3S Web of Conferences
For identifying maximum power tracking by using a solar PV system, a modified solar panel is designed with the support of reflector. Based on diameter and size, reflector is selected.
Deshmukh Rajesh Keshavrao, Shrivastav Mohit   +1 more
doaj   +1 more source

Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics

IEEE Access
Scheduling problems, which involve allocating resources to tasks over specified time periods to optimize objectives, are crucial in various fields. This work presents hyper-heuristic applications for scheduling problems, analyzing 215 peer-reviewed ...
Alonso Vela, Gerardo Humberto Valencia-Rivera, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Ivan Amaya   +4 more
doaj   +1 more source

Student Surpasses the Teacher: Apprenticeship Learning for Quadratic Unconstrained Binary Optimisation

Algorithms
This study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem.
Jack Cakebread, Warren G. Jackson, Daniel Karapetyan, Andrew J. Parkes, Ender Özcan   +4 more
doaj   +1 more source

CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search

SoftwareX, 2020
There is a colourful palette of metaheuristics for solving continuous optimisation problems in the literature. Unfortunately, it is not easy to pick a suitable one for a specific practical scenario. Moreover, oftentimes the selected metaheuristic must be
Jorge M. Cruz-Duarte, Ivan Amaya, José C. Ortiz-Bayliss, Hugo Terashima-Marín, Yong Shi   +4 more
doaj   +1 more source

A new index‐based hyper‐heuristic algorithm for global optimisation problems

IET Software, 2022
In this research study, a new combination search algorithm, based on indexing its constituent processes, is proposed to solve global optimisation problems.
Mohammad Reza Hasanzadeh, Farshid Keynia, Maliheh Hashemipour   +2 more
doaj   +1 more source

Evolution of group-theoretic cryptology attacks using hyper-heuristics

Journal of Mathematical Cryptology, 2021
In previous work, we developed a single evolutionary algorithm (EA) to solve random instances of the Anshel–Anshel–Goldfeld (AAG) key exchange protocol over polycyclic groups. The EA consisted of six simple heuristics which manipulated strings.
Craven Matthew J., Woodward John R.
doaj   +1 more source

Ensemble Move Acceptance in Selection Hyper-heuristics [PDF]

, 2016
Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyperheuristics, deciding whether to accept or reject a new solution at each step during the search process.
Kheiri, Ahmed, Mısır, Mustafa, Özcan, Ender   +2 more
openaire   +3 more sources