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Metamodeling Framework for Simultaneous Multi-Objective Optimization Using Efficient Evolutionary Algorithms
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Metamodeling Framework for Simultaneous Multi-Objective Optimization Using Efficient Evolutionary Algorithms. |
| 개인저자 | Roy, Proteek Chandan. |
| 단체저자명 | Michigan State University. Computer Science - Doctor of Philosophy. |
| 발행사항 | [S.l.]: Michigan State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 115 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085707084 |
| 학위논문주기 | Thesis (Ph.D.)--Michigan State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Deb, Kalyanmoy. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Most real-world problems are comprised of multiple conflicting objectives and solutions to those problems are multiple Pareto-optimal trade-off solutions. The main challenge of these practical problems is that the objectives and constraints do not have any closed functional forms and they are expensive for computation as well. Objectives coming from finite element analysis, computational fluid dynamics software, network flow simulators, crop modeling, weather modeling or any other simulations which involve partial differential equations are good examples of expensive problems. These problems can also be regarded as ``low-budget'' problems since only a few solution evaluations can be performed given limited time. Nevertheless, parameter estimation and optimization of objectives related to these simulations require a good number of solution evaluations to come up with better parameters or a reasonably good trade-off front. To provide an efficient search process within a limited number of exact evaluations, metamodel-assisted algorithms have been proposed in the literature. These algorithms attempt to construct a computationally inexpensive representative model of the problem, having the same global optima and thereby providing a way to carry out the optimization in metamodel space in an efficient way. Population-based methods like evolutionary algorithms have become standard for solving multi-objective problems and recently Metamodel-based evolutionary algorithms are being used for solving expensive problems. In this thesis, we would like to address a few challenges of metamodel-based optimization algorithms and propose some efficient and innovative ways to construct these algorithms. To approach efficient design of metamodel-based optimization algorithm, one needs to address the choice of metamodeling functions. The most trivial way is to build metamodels for each objective and constraint separately. But we can reduce the number of metamodel constructions by using some aggregated functions and target either single or multiple optima in each step. We propose a taxonomy of possible metamodel-based algorithmic frameworks which not only includes most algorithms from the literature but also suggests some new ones. We improve each of the frameworks by introducing trust region concepts in the multi-objective scenario and present two strategies for building trust regions. Apart from addressing the main bottleneck of the limited number of solution evaluations, we also propose efficient non-dominated sorting methods that further reduce computational time for a basic step of multi-objective optimization. We have carried out extensive experiments over all representative metamodeling frameworks and shown that each of them can solve a good number of test problems. We have not tried to tune the algorithmic parameters yet and it remains as our future work. Our theoretical analyses and extensive experiments suggest that we can achieve efficient metamodel-based multi-objective optimization algorithms for solving test as well as real-world expensive and low-budget problems. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
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