Machine Learning Enabled Fast Multi-Objective Optimization for Electrified Aviation Power System Design

Jackson, Derek; Belakaria, Syrine; Cao, Yue; Doppa, Janardhan Rao; Liu, Xiaonan

doi:10.1109/ecce44975.2020.9235599

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…We conduct experiments on benchmarks with varying numbers of input and output dimensions to show the versatility and flexibility of our method. We use several synthetic problems: ZDT-1, ZDT-2, ZDT-3 (Zitzler, Deb, and Thiele 2000), DTLZ-1, DTLZ-3, DTLZ-5 (Deb et al 2005) and three real wold problems: the gear train design problem (Deb and Srinivasan 2006;Konakovic Lukovic, Tian, and Matusik 2020), SWLLVM (Siegmund et al 2012) and Unmanned aerial vehicle power system design (Belakaria et al 2020b). More details and descriptions of problem settings are included in the Appendix.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization

Ahmadianshalchi,

Belakaria,

Doppa

2024

AAAI

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We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allowed to evaluate a batch of inputs. This problem arises in many real-world applications including penicillin production where diversity of solutions is critical. We solve this problem in the framework of Bayesian optimization (BO) and propose a novel approach referred to as Pareto front-Diverse Batch Multi-Objective BO (PDBO). PDBO tackles two important challenges: 1) How to automatically select the best acquisition function in each BO iteration, and 2) How to select a diverse batch of inputs by considering multiple objectives. We propose principled solutions to address these two challenges. First, PDBO employs a multi-armed bandit approach to select one acquisition function from a given library. We solve a cheap MOO problem by assigning the selected acquisition function for each expensive objective function to obtain a candidate set of inputs for evaluation. Second, it utilizes Determinantal Point Processes (DPPs) to choose a Pareto-front-diverse batch of inputs for evaluation from the candidate set obtained from the first step. The key parameters for the methods behind these two steps are updated after each round of function evaluations. Experiments on multiple MOO benchmarks demonstrate that PDBO outperforms prior methods in terms of both the quality and diversity of Pareto solutions.

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Section: Experiments and Resultsmentioning

confidence: 99%

Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization

Ahmadianshalchi,

Belakaria,

Doppa

2024

AAAI

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“…A large number of engineering design problems involve making design choices to optimize multiple objectives. Some examples include electric power systems design [45,8], design of aircrafts [46], and design of analog circuits [36,50], and nanoporous materials discovery [12]. The common challenges in such constrained multi-objective optimization (MOO) problems include the following.…”

Section: Introductionmentioning

confidence: 99%

Preference-Aware Constrained Multi-Objective Bayesian Optimization

Ahmadianshalchi¹,

Belakaria²,

Doppa³

2023

Preprint

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This paper addresses the problem of constrained multi-objective optimization over black-box objective functions with practitioner-specified preferences over the objectives when a large fraction of the input space is infeasible (i.e., violates constraints). This problem arises in many engineering design problems including analog circuits and electric power system design. Our overall goal is to approximate the optimal Pareto set over the small fraction of feasible input designs. The key challenges include the huge size of the design space, multiple objectives and large number of constraints, and the small fraction of feasible input designs which can be identified only after performing expensive simulations. We propose a novel and efficient preference-aware constrained multi-objective Bayesian optimization approach referred to as PAC-MOO to address these challenges. The key idea is to learn surrogate models for both output objectives and constraints, and select the candidate input for evaluation in each iteration that maximizes the information gained about the optimal constrained Pareto front while factoring in the preferences over objectives. Our experiments on two real-world analog circuit design optimization problems demonstrate the efficacy of PAC-MOO over prior methods.

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HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

Yang

et al. 2022

World Wide Web

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Machine Learning Enabled Fast Multi-Objective Optimization for Electrified Aviation Power System Design

Cited by 5 publications

References 13 publications

Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization

Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization

Preference-Aware Constrained Multi-Objective Bayesian Optimization

HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

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