Scalable and Customizable Benchmark Problems for Many-Objective Optimization

A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning

To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customiza…

面向鲁棒SLAM基准测试的可定制扰动合成

Customizable Perturbation Synthesis for Robust SLAM Benchmarking

提出可定制噪声数据合成流水线，构建Robust-SLAM基准测试，评估多模态SLAM模型对扰动的鲁棒性，揭示现有模型在真实世界扰动下的脆弱性。

Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit

Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems. In fact, many real-world multi-objective problems contain a number of constraints. To promote research on constrained multi-objective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multi-objective opti…

Scalable Many-Objective Pathfinding Benchmark Suite

Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation. We anal…

A Batched Scalable Multi-Objective Bayesian Optimization Algorithm

The surrogate-assisted optimization algorithm is a promising approach for solving expensive multi-objective optimization problems. However, most existing surrogate-assisted multi-objective optimization algorithms have three main drawbacks: 1) cannot scale well for solving problems with high dimensional decision space, 2) cannot incorporate available gradient information, and 3) do not support batch optimization. These drawbacks prevent their use for solving many real-world large scale optimization problems. This paper proposes a batched scalable multi-objective Bayesian optimization algorithm …

面向目标数量变化的动态多目标优化的可扩展基准测试套件

A Scalable Benchmark Test Suite for Dynamic Multi-Objective Optimization with a Changing Number of Objectives

针对现有基准套件在目标数量变化时目标函数隐含变化的问题，提出目标函数固定、仅活跃目标数量变化的可扩展基准测试套件，采用Minus-DTLZ和Minus-WFG公式避免退化，并通过代表性算法验证其有效性。