Advanced Multi-Objective Optimization Algorithms
143 papers on this topic
An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to...
Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems
Population-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choose the best...
R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm
Choosing an appropriate optimization algorithm is essential to achieving success in optimization challenges. Here we present a new evolutionary algorithm structure that utilizes a reinforcement...
Principled Bayesian Optimisation in Collaboration with Human Experts
Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation process. We consider...
Preference Learning from Physics-Based Feedback: Tuning Language Models to Design BCC/B2 Superalloys
We apply preference learning to the task of language model-guided design of novel structural alloys. In contrast to prior work that focuses on generating stable inorganic crystals, our approach...
Improving Pareto Front Learning via Multi-Sample Hypernetworks
Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the...
Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results
Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its...
Bayesian Optimization with High-Dimensional Outputs
Bayesian Optimization is a sample-efficient black-box optimization procedure that is typically applied to problems with a small number of independent objectives. However, in practice we often wish...
Scalarisation-based risk concepts for robust multi-objective optimisation
Robust optimisation is a well-established framework for optimising functions in the presence of uncertainty. The inherent goal of this problem is to identify a collection of inputs whose outputs...
Enhanced Multi-Objective A* Using Balanced Binary Search Trees
This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a graph where the goal is to find a set of Pareto-optimal solutions from a start node to a destination in the graph. A...
Using Scalarizations for the Approximation of Multiobjective Optimization Problems: Towards a General Theory
We study the approximation of general multiobjective optimization problems with the help of scalarizations. Existing results state that multiobjective minimization problems can be approximated...
Stalling in Space: Attractor Analysis for any Algorithm
Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisation algorithms. Local...
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces
Quality Diversity (QD) algorithms have been proposed to search for a large collection of both diverse and high-performing solutions instead of a single set of local optima. While early QD...
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax
The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness. In the context of multi-objective optimization this constraint...
Computational and Exploratory Landscape Analysis of the GKLS Generator
The GKLS generator is one of the most used testbeds for benchmarking global optimization algorithms. In this paper, we conduct both a computational analysis and the Exploratory Landscape Analysis...
3-Objective Pareto Optimization for Problems with Chance Constraints
Evolutionary multi-objective algorithms have successfully been used in the context of Pareto optimization where a given constraint is relaxed into an additional objective. In this paper, we...
Statistically Distinct Plans for Multi-Objective Task Assignment
We study the problem of finding statistically distinct plans for stochastic planning and task assignment problems such as online multi-robot pickup and delivery (MRPD) when facing multiple...
Domain Knowledge Injection in Bayesian Search for New Materials
In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that accommodates domain knowledge to tune exploration in the search space. Bayesian optimization has recently emerged as a...