Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
Solving complex optimization problems can be painstakingly difficult endeavor considering multiple and conflicting design goals. A growing trend in utilizing meta-heuristic algorithms to solve these problems has been observed as they have shown considerable success in dealing with tradeoffs between...
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| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/23080/ http://umpir.ump.edu.my/id/eprint/23080/1/Enhancing%20generality%20of%20meta-heuristic%20algorithms%20through%20adaptive%20selection%20and%20hybridization.pdf |
| _version_ | 1848821724992241664 |
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| author | Kamal Z., Zamli |
| author_facet | Kamal Z., Zamli |
| author_sort | Kamal Z., Zamli |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Solving complex optimization problems can be painstakingly difficult endeavor considering multiple and conflicting design goals. A growing trend in utilizing meta-heuristic algorithms to solve these problems has been observed as they have shown considerable success in dealing with tradeoffs between conflicting design goals. Many meta-heuristic algorithms have been developed to date (e.g. Simulated Annealing (SA), Particle Swarm Optimization (PSO), Teaching Learning based Optimization (TLBO), Grey Wolf Optimizer(GWO) to name a few). Much of these algorithms have adopted elegant metaphors (e.g. heating and cooling of metals in the case of SA and swarming of flocking birds in the case of PSO) from nature in order to derive the mathematical models for generating the solution as well as provides control over their exploration (i.e. sufficient roaming of the search space) and exploitation (i.e. using known knowledge of the surroundings). In line with the no free lunch theorem (), this paper argues that rather than focusing on designing new algorithm, new research should focus on adaptive hybridization of meta-heuristics algorithms in order to compensate the limitation of one with the strengths of another. In this paper, we review the meta-heuristic and hyper-heuristic algorithms in order to highlight the current-state-of-the-arts and suggest areas for future research. |
| first_indexed | 2025-11-15T02:29:54Z |
| format | Conference or Workshop Item |
| id | ump-23080 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:29:54Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-230802019-04-22T03:14:58Z http://umpir.ump.edu.my/id/eprint/23080/ Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization Kamal Z., Zamli QA76 Computer software Solving complex optimization problems can be painstakingly difficult endeavor considering multiple and conflicting design goals. A growing trend in utilizing meta-heuristic algorithms to solve these problems has been observed as they have shown considerable success in dealing with tradeoffs between conflicting design goals. Many meta-heuristic algorithms have been developed to date (e.g. Simulated Annealing (SA), Particle Swarm Optimization (PSO), Teaching Learning based Optimization (TLBO), Grey Wolf Optimizer(GWO) to name a few). Much of these algorithms have adopted elegant metaphors (e.g. heating and cooling of metals in the case of SA and swarming of flocking birds in the case of PSO) from nature in order to derive the mathematical models for generating the solution as well as provides control over their exploration (i.e. sufficient roaming of the search space) and exploitation (i.e. using known knowledge of the surroundings). In line with the no free lunch theorem (), this paper argues that rather than focusing on designing new algorithm, new research should focus on adaptive hybridization of meta-heuristics algorithms in order to compensate the limitation of one with the strengths of another. In this paper, we review the meta-heuristic and hyper-heuristic algorithms in order to highlight the current-state-of-the-arts and suggest areas for future research. IEEE 2018 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23080/1/Enhancing%20generality%20of%20meta-heuristic%20algorithms%20through%20adaptive%20selection%20and%20hybridization.pdf Kamal Z., Zamli (2018) Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization. In: 1st International Conference on Information and Communications Technology (ICOIACT 2018) , 6-7 March 2018 , Grand ZuriYogyakarta, Indonesia. pp. 67-71., 2018. ISBN 978-153860954-5 (Published) https://ieeexplore.ieee.org/abstract/document/8350825 |
| spellingShingle | QA76 Computer software Kamal Z., Zamli Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization |
| title | Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization |
| title_full | Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization |
| title_fullStr | Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization |
| title_full_unstemmed | Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization |
| title_short | Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization |
| title_sort | enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/23080/ http://umpir.ump.edu.my/id/eprint/23080/ http://umpir.ump.edu.my/id/eprint/23080/1/Enhancing%20generality%20of%20meta-heuristic%20algorithms%20through%20adaptive%20selection%20and%20hybridization.pdf |