Advanced Multi-Objective Optimization Algorithms
143 papers on this topic
Multi-Objective Reinforcement Learning for Power Grid Topology Control
Transmission grid congestion increases as the electrification of various sectors requires transmitting more power. Topology control, through substation reconfiguration, can reduce congestion but...
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
Optimizing multiple competing black-box objectives is a challenging problem in many fields, including science, engineering, and machine learning. Multi-objective Bayesian optimization (MOBO) is a...
SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration
Surrogate models are of high interest for many engineering applications, serving as cheap-to-evaluate time-efficient approximations of black-box functions to help engineers and practitioners make...
FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) aims to optimize policies in the presence of conflicting objectives, where linear scalarization is commonly used to reduce vector-valued returns into...
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality,...
Analysing the Robustness of NSGA-II under Noise
Runtime analysis has produced many results on the efficiency of simple evolutionary algorithms like the (1+1) EA, and its analogue called GSEMO in evolutionary multiobjective optimisation (EMO)....
Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization
Multi-objective optimization (MOO) arises in many real-world applications where trade-offs between competing objectives must be carefully balanced. In the offline setting, where only a static...
Data-driven decision-focused surrogate modeling
We introduce the concept of decision-focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real-time settings. The proposed data-driven framework...
Multi-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem
Evolutionary algorithms are particularly effective for optimisation problems with dynamic and stochastic components. We propose multi-objective evolutionary approaches for the knapsack problem...
MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning
Multi-objective Reinforcement Learning (MORL) seeks to develop policies that simultaneously optimize multiple conflicting objectives, but it requires extensive online interactions. Offline MORL...
Constrained Bayesian Optimization Under Partial Observations: Balanced Improvements and Provable Convergence
The partially observable constrained optimization problems (POCOPs) impede data-driven optimization techniques since an infeasible solution of POCOPs can provide little information about the...
Information-theoretic Bayesian Optimization: Survey and Tutorial
Several scenarios require the optimization of non-convex black-box functions, that are noisy expensive to evaluate functions with unknown analytical expression, whose gradients are hence not...
When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming
Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more...
JCLEC-MO: a Java suite for solving many-objective optimization engineering problems
Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts...
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e.g., it is fundamental for multiple avenues...
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution....
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for...
Multi-objective Feature Selection with Missing Data in Classification
Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a+ bi-objective optimization problem whose objectives are: 1) classification accuracy; 2)...