Advanced Multi-Objective Optimization Algorithms

Neueste Paper

• PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

  Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger · 2. Oktober 2026

  Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes these priorities into a fixed scalar reward at training time. We present PROMO (Preference-Conditioned Multi-Objective R…

• Adapting Nonstationary Multi-output Gaussian Processes to Bayesian Optimization

  Zikai Xie · 29. September 2026

  Multi-objective Bayesian optimization (MOBO) commonly relies on independent Gaussian processes (GPs) with stationary kernels, limiting its ability to represent nonstationary structure and share information between objectives. However, expressive nonstationary GPs do not necessarily make reliable BO …

• Energy-aware frugal Bayesian optimization

  Gaston Plat, Paul Saves, Nathalie Bartoli, Thierry Lefebvre, Joseph Morlier · 29. September 2026

  Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled architecture and multidisciplinary design optimization problems are difficult to address, even with sample-efficient Bay…

• Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero

  Zipei Yu, Yue-Jiao Gong, Zeyuan Ma, Yuncheng Jiang, Zhiguang Cao · 29. September 2026

  Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages the learnable algorithm design policy at meta level to ensure the performance and generalization improvement on the low-level optimization task. While M…

• MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization

  Lucas Palazzolo, Micka\"el Binois, La\"etitia Giraldi · 25. September 2026

  Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations…

• Generative Evolutionary Design of Voxel-Based Soft Robots with Provable Optimality

  Junru Song, Huan Xiao, Yang Yang, Guozhen Li, Wei Peng, Xiaoya Zhang, Tingsong Jiang, Weien Zhou, Ying Wen, Feifei Wang, Wen Yao · 25. September 2026

  Voxel-based soft robots (VSRs) present a promising avenue for developing artificial organisms with lifelike intelligence. However, the vast design spaces and expensive evaluations substantially challenge their design optimization. Here we develop MISCO, a novel evolutionary framework empowered by de…

• BOBA: Dynamic Bayesian Optimization through Bayesian Active Inference

  Merlin Angel Kelly, Rishan Patel, Alexander Thomas, Ziyue Zhu, Zikun Quan, Tom Carlson, Youngjun Cho · 23. September 2026

  Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing optimal locations to shift continuously. Existing dynamic BO (DBO) methods using standard acquisition functions such as Upper Confidence Bound (UCB) fai…

• In-Context Guidance: Learning Inter-Task Synergies via Numerical Foundational Models for Few-Shot Multitask Optimization

  Tingyang Wei, Haofeng Wu, Jiao Liu, Zhao Wei, Puay Siew Tan, Yew-Soon Ong · 23. September 2026

  Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation under limited evaluation budgets, leading to negative transfer. This paper introduces In-Context Guidance Multitask Optimization (ICG-MTO), a novel f…

• Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systems

  Paul Saves, Thierry Lefebvre, Nathalie Bartoli, Jasper Bussemaker, Nikolaos Kalliatakis, Nabih Naeem, Prajwal Prakasha · 22. September 2026

  Developing innovative system architectures increasingly relies on advanced modeling and optimization techniques to frame the architecting process and define the corresponding computational problems. For complex System-of-Systems (SoS), high-fidelity multiphysics and multidisciplinary simulations are…

• Multi-Objective Hyperparameter Search via Damped Gauss--Newton Optimization

  Qinwu Xu, Yifan Jiang · 21. September 2026

  We study hyperparameter optimization (HPO) from a numerical-optimization perspective and propose a multi-objective damped Newton--Gauss--Newton search method. Rather than perturbing each hyperparameter separately or treating model evaluations as independent trials, the method uses performance change…

• CrystalMO-TuRBO: Multi-Objective Trust-Region Bayesian Optimization for High-precision Joint Crystal Structure Refinement

  Joseph Agada, Yishu Wang, Arpan Biswas · 18. September 2026

  Crystal structure refinement is a fundamental inverse problem in materials characterization, where structural parameters are optimized to reproduce experimental diffraction data. Conventional approaches, such as least-squares and likelihood-based optimization, rely on local search and often struggle…

• Expected Hypervolume Maximization for Multiobjective Optimization under Uncertainties

  Victor Trappler (Mines Saint-\'Etienne MSE, LIMOS, FAYOL-ENSMSE, FAYOL-ENSMSE) · 18. September 2026

  The problem of multiobjective optimization under uncertainties is often approached by taking the expectation of each objective. In this work, we propose instead to formulate this as a Bayesian decision problem and to rely on the expected value of the hypervolume, which is to be maximized with respec…

• Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization

  Donney Fan, Colin Doumont, Aleksandra Kalisz, Paul Duckworth, Jacob R. Gardner, Henry Moss, Geoff Pleiss · 18. September 2026

  Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of candidates to experimentally validate. While Bayesian optimization (BO) is a natural fit for this setting, as it uses pas…

• Bayesian Optimization with Rich Auxiliary Information via LLMs

  Tejus Gupta, Efe Mert Karag\"ozl\"u, Rohit Sonker, Barnab\'as P\'oczos, Jeff Schnieder · 18. September 2026

  Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet many real-world optimization problems contain substantially richer information than function evaluations alone. Examples include training curves in hyperparameter optimization, expert notes and images in scie…

• Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models

  Niki Triantafyllou, Andrea Bernardi, Maria M. Papathanasiou · 16. September 2026

  Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address these challenges, we present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framewo…

• A Dynamic Aggregation Strategy Enhanced Efficient Global Optimization Algorithm for Solving High-Dimensional Turbomachinery Design Problems

  Qineng Wang, Zhendong Guo, Yun Chen, Guangjian Ma, Liming Song, Jun Li · 16. September 2026

  In order to solve the high-dimensional ($d \geq 30$) expensive black-box problems within budget, an efficient global optimization (EGO) algorithm with a dynamic aggregation strategy is proposed, labeled as DA-EGO. Specifically, the DA-EGO decomposes the original high-dimensional design space into a …

• Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation

  Chao Jiang, Yueling Huang, Miqing Li · 15. September 2026

  Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to th…

• SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning

  Zebin Chen, Fei Xing, Yang Chen, Hua Liu, Andy HF Chow, Yuhua Qian, Yu Zhang · 14. September 2026

  Multi-task learning (MTL) requires navigating unavoidable trade-offs among competing objectives. This paradigm is frequently formulated as multi-objective optimization (MOO), where the scalarization is favored to reduce an MOO problem to a single objective. We empirically find that existing merit-fu…

• Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfer

  Tingyang Wei, Haofeng Wu, Ananda Phan Iman, Zhao Wei, Jiao Liu, Yew-Soon Ong · 11. September 2026

  Applying knowledge transfer across multiple optimization tasks, multitask optimization (MTO) emerges as a promising approach to solving synergistic optimization tasks simultaneously. However, the development of effective knowledge transfer mechanisms in MTO fundamentally relies on aligning elite sol…

• Constraint-Aware Discrete Black-Box Optimization Using Tensor Decomposition

  Keisuke Onoue, Ryosuke Kojima · 10. September 2026

  Discrete black-box optimization is often addressed using approaches such as Sequential Model-Based Optimization (SMBO), which aims to improve sample efficiency by fitting surrogate models that approximate a costly objective function over a discrete search space. In many real-world problems, the set …

• A Better Spur Should Start From Each Objective

  Shanwen Mao, Hao Zhang, Guangtao nie, Zhiheng Li, Huimu Wang, Sulong Xu, Gu Simiu · 9. September 2026

  Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing traditional linear scalarization to experience severe metric oscillations. To address optimization conflicts among multiple objectives in real-world…

• MOAE: Multi-Objective Agent Evolution with Pareto-Preserving Search

  Hengle Jiang, Qijun Cai, Ziying Luo, Ke Tang · 9. September 2026

  As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task completion accuracy but also perform well in interaction quality, safety, and efficiency, raising a central question: can these objectives be optimized simu…

• Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search

  Zhiliang Chen, Sebastian Ament, David Eriksson, Maximilian Balandat, Eytan Bakshy, Jihao Andreas Lin · 2. September 2026

  Optimal hyperparameter scaling laws describe how the best hyperparameters for large language model (LLM) training change with model and data scale, enabling practitioners to predict optimal configurations at production scales without expensive large-scale tuning. However, estimating these scaling la…

• Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design

  Chuhan Yang, Chenxi Wang, Linhan Wu, Yuyang Liu · 31. August 2026

  Early discovery of at least one valid design satisfying a target requirement is a central objective in failure-prone closed-loop inverse design. A natural batch baseline ranks candidates by a product-form marginal valid-hit score, but selecting the highest-ranked candidates independently can produce…

• Gradient-based Sample Selection for Faster Bayesian Optimization

  Qiyu Wei, Haowei Wang, Zirui Cao, Songhao Wang, Richard Allmendinger, Mauricio A \'Alvarez · 27. August 2026

  Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-budget problems due to the cubic complexity of fitting the Gaussian process (GP) surrogate model. In large-budget scenarios, directly employing the standard GP…