Single and Multi-Objective Optimization Benchmark Problems Focusing on
Human-Powered Aircraft Design
- URL: http://arxiv.org/abs/2312.08953v3
- Date: Sun, 25 Feb 2024 03:10:32 GMT
- Title: Single and Multi-Objective Optimization Benchmark Problems Focusing on
Human-Powered Aircraft Design
- Authors: Nobuo Namura
- Abstract summary: This paper introduces a novel set of benchmark problems aimed at advancing research in both single and multi-objective optimization.
These benchmark problems are unique in that they incorporate real-world design considerations such as fluid dynamics and material mechanics.
We propose three difficulty levels and a wing segmentation parameter in these problems, allowing for scalable complexity to suit various research needs.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper introduces a novel set of benchmark problems aimed at advancing
research in both single and multi-objective optimization, with a specific focus
on the design of human-powered aircraft. These benchmark problems are unique in
that they incorporate real-world design considerations such as fluid dynamics
and material mechanics, providing a more realistic simulation of engineering
design optimization. We propose three difficulty levels and a wing segmentation
parameter in these problems, allowing for scalable complexity to suit various
research needs. The problems are designed to be computationally reasonable,
ensuring short evaluation times, while still capturing the moderate
multimodality of engineering design problems. Our extensive experiments using
popular evolutionary algorithms for multi-objective problems demonstrate that
the proposed benchmarks effectively replicate the diverse Pareto front shapes
observed in real-world problems, including convex, linear, concave, and
inverted triangular forms. The benchmark problems' source codes are publicly
available for wider application in the optimization research community.
Related papers
- Non-Myopic Multi-Objective Bayesian Optimization [64.31753000439514]
We consider the problem of finite-horizon sequential experimental design to solve multi-objective optimization problems.
This problem arises in many real-world applications, including materials design.
We propose the first set of non-myopic methods for MOO problems.
arXiv Detail & Related papers (2024-12-11T04:05:29Z) - Vehicle Suspension Recommendation System: Multi-Fidelity Neural Network-based Mechanism Design Optimization [4.038368925548051]
Vehicle suspensions are designed to improve driving performance and ride comfort, but different types are available depending on the environment.
Traditional design process is multi-step, gradually reducing the number of design candidates while performing costly analyses to meet target performance.
Recently, AI models have been used to reduce the computational cost of FEA.
arXiv Detail & Related papers (2024-10-03T23:54:03Z) - Optimization-Driven Adaptive Experimentation [7.948144726705323]
Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization.
Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible, and static designs remain the de facto standard.
We present a mathematical programming formulation that can flexibly incorporate a wide range of objectives, constraints, and statistical procedures.
arXiv Detail & Related papers (2024-08-08T16:29:09Z) - MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with
Known Pareto Front [71.19090689055054]
Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts.
We propose a tunable benchmark generator, together with a dedicated solver that provably computes the true Pareto front of the resulting instances.
We show how to characterize the optimal plans for a constrained version of the problem, and then show how to reduce the general problem to the constrained one.
arXiv Detail & Related papers (2023-04-28T07:09:23Z) - Numerical Methods for Convex Multistage Stochastic Optimization [86.45244607927732]
We focus on optimisation programming (SP), Optimal Control (SOC) and Decision Processes (MDP)
Recent progress in solving convex multistage Markov problems is based on cutting planes approximations of the cost-to-go functions of dynamic programming equations.
Cutting plane type methods can handle multistage problems with a large number of stages, but a relatively smaller number of state (decision) variables.
arXiv Detail & Related papers (2023-03-28T01:30:40Z) - Constrained multi-objective optimization of process design parameters in
settings with scarce data: an application to adhesive bonding [48.7576911714538]
Finding the optimal process parameters for an adhesive bonding process is challenging.
Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem.
In this research, we successfully applied specific machine learning techniques to emulate the objective and constraint functions.
arXiv Detail & Related papers (2021-12-16T10:14:39Z) - Multi-Objective Constrained Optimization for Energy Applications via
Tree Ensembles [55.23285485923913]
Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives.
In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions.
This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems.
arXiv Detail & Related papers (2021-11-04T20:18:55Z) - Characterization of Constrained Continuous Multiobjective Optimization
Problems: A Feature Space Perspective [0.0]
constrained multiobjective optimization problems (CMOPs) are still unsatisfactory understood and characterized.
We propose 29 landscape features (of which 19 are novel) to characterize CMOPs.
We compare eight frequently used artificial test suites against a recently proposed suite consisting of real-world problems based on physical models.
arXiv Detail & Related papers (2021-09-09T21:21:57Z) - An Easy-to-use Real-world Multi-objective Optimization Problem Suite [7.81768535871051]
We present a multi-objective optimization problem suite consisting of 16 bound-constrained real-world problems.
4 out of the 16 problems are multi-objective mixed-integer optimization problems.
We analyze the performance of six representative evolutionary multi-objective optimization algorithms on the 16 problems.
arXiv Detail & Related papers (2020-09-27T15:11:08Z) - Scalable Differentiable Physics for Learning and Control [99.4302215142673]
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
We develop a scalable framework for differentiable physics that can support a large number of objects and their interactions.
arXiv Detail & Related papers (2020-07-04T19:07:51Z) - Scalable and Customizable Benchmark Problems for Many-Objective
Optimization [0.0]
We propose a parameterized generator of scalable and customizable benchmark problems for many-objective problems (MaOPs)
It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features.
arXiv Detail & Related papers (2020-01-26T12:39:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.