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Metaheuristic search behaviour characterisation
Thesis (MSc)--Stellenbosch University, 2025.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867613806718353408 |
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| access_status_str | Open Access |
| author | Hayward, Lauren P. |
| author2 | Engelbrecht, Andries |
| author_browse | Engelbrecht, Andries Hayward, Lauren P. |
| author_facet | Engelbrecht, Andries Hayward, Lauren P. |
| author_sort | Hayward, Lauren P. |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2025. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/134629 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:42:00.137Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/134629 Metaheuristic search behaviour characterisation Hayward, Lauren P. Engelbrecht, Andries Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Computer Science Division. Mathematical optimization Heuristic algorithms Computational intelligence Swarm intelligence Searching behavior Thesis (MSc)--Stellenbosch University, 2025. Hayward, L. P. 2025. Metaheuristic Search Behaviour Characterisation. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/31678c98-d3ee-4349-acae-3d9ea78755e6 ENGLISH ABSTRACT: Metaheuristics are a type of optimisation algorithm that employ techniques independent of the optimisation problem. In many famous metaheuristics, the ‘meta’ refers to techniques inspired by nature. Just as natural phenom-ena may be observed without fully understanding their mechanics, the perfor-mance of metaheuristics on specific problems is often not fully u nderstood. If metaheuristics are to be widely used and trusted, they must be better under-stood. One avenue of understanding is through search behaviour characteri-sation, where the search behaviour of a metaheuristic is the manner in which it searches the problem landscape. This thesis makes several contributions to improving the understanding of metaheuristic search behaviour. Numerical methods of characterising metaheuristic search behaviour are surveyed and analysed. The analysis investigates which of the search behaviour indicators are capable of distinguishing between different search behaviours, and which of the search behaviour indicators are distinct from other indicators. Following on this analysis, a search behaviour benchmarking suite is published through which metaheuristic search behaviour may be characterised. Beyond providing the calculation of search behaviour indicators, the tool provides experiment-backed guidelines on performing search behaviour characterising experiments. Finally, the search behaviour benchmarking suite is used to characterise a host of metaphor-based metaheuristic algorithms. The analysis reveals limited nov-elty in the search behaviour of these algorithms. AFRIKAANSE OPSOMMING: ’n Metaheuristiek is ’n tipe optimeringsalgoritme wat gebruik maak van tegnieke wat onafhanklik van die optimeringsprobleem is. In baie bekende metaheuristieke verwys die ‘meta’ voorvoegsel na tegnieke wat deur die natuur geïnspireer is. Net soos ’n natuurverksynsel waargeneem kan word sonder om dit ten volle te verstaan, word die uitkomstes van metaheuristieke op spesifieke probleme dikwels nie ten volle verstaan nie. Maar as metaheuristieke algemeen gebruik en vertrou gaan word, moet dit beter verstaan word. Een manier om dit beter te verstaan is deur soekgedragkarakterisering, waar die soekgedrag van ’n metaheuristiek die wyse is waarop dit die probleemlandskap ondersoek. Hierdie tesis lewer verskeie bydraes om die begrip van metaheuristiese soekgedrag te verbeter. Numeriese metodes vir die karakterisering van metaheuristiese soekgedrag word ondersoek en ontleed. Die analise ondersoek watter van die soekgedrag-aanwysers in staat is om tussen verskillende soekgedrag te onderskei en watter van die soekgedrag-aanwysers van mekaar verskil. Na aanleiding van hierdie analise word ’n soekgedragmaatstafversameling gepubliseer, en daardeur kan metaheuristiese soekgedrag gekarakteriseer word. Die maatstafversameling verskaf nie net die berekening van soekgedrag-aanwysers nie, maar verskaf ook die eksperimentgesteunde riglyne vir die uitvoering van soekgedragkarakterisering eksperimente. Laastens word die soekgedragmaatstafversameling gebruik om ’n menigte natuur-geïnspireerde metaheuristiese algoritmes te karakteriseer. Die ontleding toon dat daar beperkte nuutheid in die soekgedrag van hierdie algoritmes is. Masters 2025-12-19T09:15:36Z 2025-12-19T09:15:36Z 2025-12 Thesis https://scholar.sun.ac.za/handle/10019.1/134629 en Stellenbosch University xv, 142 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Mathematical optimization Heuristic algorithms Computational intelligence Swarm intelligence Searching behavior Hayward, Lauren P. Metaheuristic search behaviour characterisation |
| title | Metaheuristic search behaviour characterisation |
| title_full | Metaheuristic search behaviour characterisation |
| title_fullStr | Metaheuristic search behaviour characterisation |
| title_full_unstemmed | Metaheuristic search behaviour characterisation |
| title_short | Metaheuristic search behaviour characterisation |
| title_sort | metaheuristic search behaviour characterisation |
| topic | Mathematical optimization Heuristic algorithms Computational intelligence Swarm intelligence Searching behavior |
| url | https://scholar.sun.ac.za/handle/10019.1/134629 |
| work_keys_str_mv | AT haywardlaurenp metaheuristicsearchbehaviourcharacterisation |