| Meta-zoo-heuristic Algorithms | |
| Fong, Simon; Ariwa, E; Pichappan, P | |
| 2017 | |
| Conference Name | 2017 SEVENTH INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING TECHNOLOGY (INTECH 2017) |
| Pages | 3-8 |
| Publication Place | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| Publisher | IEEE |
| Abstract | Metaheuristic algorithms (MH's) are referred to algorithms which has a two-level design 'meta' is upper-level procedure that controls the underlying 'heuristic' which learns and improves a solution iteratively until a sufficiently good solution is obtained for an optimization problem. Since 2008, MH's started to receive attention from researchers around the globe. Variants and new species of MH algorithms emerged. Most of them are claimed to be inspired from the nature or biology. The logics of the search algorithms are mimicked front animal behaviors or nature phenomenon. However, the necessity for creating more new species of such algorithms is doubted. Instead of inventing extra MH's which are similar to one another, we start to ponder if several classical MH's can be used together or in an ensemble. In this paper, the possibilities of putting several MH's into an ensemble are discussed. Different from ensemble in machine learning, we coin this unique collection of Mil's which may fuse together or function cooperatively in solving optimization problems, 'meta-zoo-heuristic'. The term 'zoo' here simply means that the selected MH's are to be kept under control. A preliminary simulation test is conducted, which demonstrates how suitable MH's are selected for a specific problem to solve. |
| Keyword | Keywords Meta-heuristics bio-inspired optimization algorithms ensemble optimization |
| URL | View the original |
| Indexed By | CPCI |
| Language | 英语 |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Hardware & Architecture ; Computer Science, Software Engineering ; Computer Science, Theory & Methods |
| WOS ID | WOS:000425919500002 |
| The Source to Article | WOS |
| Fulltext Access | |
| Citation statistics | |
| Document Type | Conference paper |
| Collection | University of Macau |
| Recommended Citation GB/T 7714 | Fong, Simon,Ariwa, E,Pichappan, P. Meta-zoo-heuristic Algorithms[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:3-8. |
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