Name
Title | ||
|---|---|---|
Kriging-Assisted Teaching-Learning-Based Optimization (Ktlbo) To Solve Computationally Expensive Constrained Problems |
Abstract | ||
|---|---|---|
In this paper, a novel algorithm KTLBO is presented to achieve computationally expensive constrained optimization. In KTLBO, Kriging is adopted to develop dynamically updated surrogate models for costly objective and inequality constraints. A data managing method aiming at solving expensive constrained problems is developed to archive, classify and update expensive samples, where a penalty function is set to adaptively select elite individuals. Moreover, based on the Teaching-Learning-based Optimization (TLBO), a Krigingassisted two-phase optimization framework is presented to alternately conduct local and global searches. In Kriging-assisted Teaching and Learning Phases, two different prescreening operators considering the probability of feasibility are respectively proposed to select the high-quality samples around the present best solution and the samples exhibiting better space-filling performance, as an attempt to balance exploitation of surrogates and exploration of unknown area. In brief, KTLBO retains the meta-heuristic search mechanism of TLBO while adopting Kriging to accelerate its search, thereby acting as a novel idea for surrogate-assisted constrained optimization. Lastly, KTLBO is compared with 6 well-known methods on 27 benchmark cases, and then its significant advantages in expensive constrained optimization are verified. Furthermore, KTLBO is adopted to design the structure of a Blended-Wing-Body underwater glider, and the satisfactory solution is yielded. (C) 2020 Elsevier Inc. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2020.09.073 | INFORMATION SCIENCES |
Keywords | DocType | Volume |
Surrogate models, Computationally expensive, Teaching-Learning-based Optimation, Constrained | Journal | 556 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huachao Dong | 1 | 4 | 2.08 |
| Peng Wang | 2 | 52 | 27.42 |
| Chongbo Fu | 3 | 1 | 0.35 |
| Song Bao-wei | 4 | 16 | 5.95 |