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Density Function-Based Trust Region Algorithm for Approximating Pareto Front of Black-Box Multiobjective Optimization Problems
AbstractIn this paper, we consider a black-box multiobjective optimization problem, whose objective functions are computationally expensive. We...
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An Adaptive Consensus Based Method for Multi-objective Optimization with Uniform Pareto Front Approximation
In this work we are interested in stochastic particle methods for multi-objective optimization. The problem is formulated via scalarization using...
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Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling
For an efficient upconvert of the Pareto front resolution by utilizing a known candidate solution set, this paper proposed an algorithm that built...
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PMDRL: Pareto-front-based multi-objective deep reinforcement learning
Most reinforcement learning research aims to optimize agents’ policies for a single objective. However, many real-world applications are inherently...
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A Pareto front estimation-based constrained multi-objective evolutionary algorithm
The balance of convergence, diversity, and feasibility plays a pivotal role in constrained multi-objective optimization problems. To address this...
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A self-driving laboratory advances the Pareto front for material properties
Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front...
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PROUD: PaRetO-gUided diffusion model for multi-objective generation
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent...
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Parameter Analysis of Stability of the Pareto Front for Optimal Conditions of Catalytic Processes
AbstractMulti criteria optimization of complicated catalytic reactions based on a kinetic model is relevant for both manufacturing and laboratory...
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Machine learning-based framework to cover optimal Pareto-front in many-objective optimization
One of the crucial challenges of solving many-objective optimization problems is uniformly well covering of the Pareto-front (PF). However, many the...
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Pareto Front Estimation Using Unit Hyperplane
This work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front...
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Pareto optimization with small data by learning across common objective spaces
In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF) as the number of points scales exponentially with...
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Pareto-efficient designs for multi- and mixed-level supersaturated designs
Supersaturated designs are used in science and engineering to efficiently explore a large number of factors with a limited number of runs. It is not...
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Pareto optimal driven automation framework for quantitative microstructure simulation towards spinodal decomposition
In this study, we developed a Pareto optimal driven automation framework for quantitative Cahn–Hilliard simulation of spinodal decomposition...
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Multi-objective infinite horizon optimal control problems: characterization of the Pareto fronts and Pareto solutions
In this paper, multi-objective infinite horizon optimal control problems with state constraints are investigated. First, a mono-objective auxiliary...
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Identifying steep pareto fronts in multicomponent adsorption using a novel elliptical method
Multicomponent adsorption processes are affected by both mixture and process variables viz. feed composition, pH, adsorbent dosage, and adsorbent...
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Identifying Pareto-optimal seismic rehabilitation strategies for water distribution networks considering decision maker’s risk attitudes
There is limited existing research that identifies the optimum rehabilitation strategy, taking utility decision-makers’ risk attitudes into account....
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Privacy-Preserving Split Learning via Pareto Optimal Search
With the rapid development of deep learning, it has become a trend for clients to perform split learning with an untrusted cloud server. The models...
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Tuning parameters of Apache Spark with Gauss–Pareto-based multi-objective optimization
When there is a need to make an ultimate decision about the unique features of big data platforms, one should note that they have configurable...
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Transmission expansion planning based on Pareto dominance considering load variation and wind power
This paper explores the impact of uncertainties related to load and renewable sources on the multi-objective transmission expansion planning (TEP)...
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Pareto optimization of SPECT acquisition and reconstruction settings for 177Lu activity quantification
BackgroundThe aim was to investigate the noise and bias properties of quantitative 177 Lu-SPECT with respect to the number of projection angles, and...
