dc.contributor.author |
Li, Xiaodong
|
|
dc.contributor.author |
Epitropakis, Michael G.
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|
dc.contributor.author |
Deb, Kalyanmoy
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|
dc.contributor.author |
Engelbrecht, Andries P.
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|
dc.date.accessioned |
2017-08-28T09:09:16Z |
|
dc.date.available |
2017-08-28T09:09:16Z |
|
dc.date.issued |
2017-08 |
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dc.description.abstract |
Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specifically-designed diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. This paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, this paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multiobjective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, this paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving. |
en_ZA |
dc.description.department |
Computer Science |
en_ZA |
dc.description.librarian |
hj2017 |
en_ZA |
dc.description.uri |
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235 |
en_ZA |
dc.identifier.citation |
Li, X.D., Epitropakis, M.G., Deb, K. & Engelbrecht, A. 2017, 'Seeking multiple solutions : an updated survey on niching methods and their applications', IEEE Transactions on Evolotionary Computation, vol. 21, no. 4, pp. 518-538. |
en_ZA |
dc.identifier.issn |
1089-778X (online) |
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dc.identifier.other |
10.1109/TEVC.2016.2638437 |
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dc.identifier.uri |
http://hdl.handle.net/2263/62113 |
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dc.language.iso |
en |
en_ZA |
dc.publisher |
Institute of Electrical and Electronics Engineers |
en_ZA |
dc.rights |
© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
en_ZA |
dc.subject |
Multimodal optimization (MMO) |
en_ZA |
dc.subject |
Truss structures |
en_ZA |
dc.subject |
Feature selection |
en_ZA |
dc.subject |
Evolutionary computation |
en_ZA |
dc.subject |
Meta-heuristics |
en_ZA |
dc.subject |
Multisolution methods |
en_ZA |
dc.subject |
Global optimization |
en_ZA |
dc.subject |
Niching methods |
en_ZA |
dc.subject |
Dynamic environments |
en_ZA |
dc.subject |
Swarm intelligence |
en_ZA |
dc.subject |
Multi-objective optimization |
en_ZA |
dc.subject |
Evolutionary algorithm |
en_ZA |
dc.subject |
Genetic algorithm |
en_ZA |
dc.subject |
Artificial neural networks |
en_ZA |
dc.title |
Seeking multiple solutions : an updated survey on niching methods and their applications |
en_ZA |
dc.type |
Postprint Article |
en_ZA |