NeurGO: Learning to Generate Elite Candidates for Meta-Black-Box Expensive Optimization

Expensive black-box optimization is ubiquitous in science and engineering, where function evaluations are costly and the evaluation budget is limited. Traditional evolutionary algorithms and Meta-BlackBox Optimization (MetaBBO) approaches typically consume most evaluations on candidate selection, often wasting precious budget on inferior solutions. Although surrogate-assisted evolution and Bayesian optimization aim to reduce evaluations through surrogate models, constructing an accurate global model from limited data remains challenging, and model bias can easily trap the search in local optima. To overcome these limitations, we propose NeurGO, a generative MetaBBO framework that directly synthesizes elite candidates from historical population states. Specifically, we employ an attention-based encoder to capture the population-level search trend and condition a decoder on this representation to generate high-quality candidates, avoiding the expensive evaluation of large offspring pools. We then design a quality-diversity loss to maintain solution quality and population diversity throughout the search. Through extensive benchmarking on CEC 2008 and the COCO BBOB test suites, our method achieves better optimization performance under the same evaluation budget and exhibits faster convergence.

Composite Indicator-Guided Infilling Sampling for Expensive Multi-Objective Optimization

In expensive multi-objective optimization, where the evaluation budget is strictly limited, selecting promising candidate solutions for expensive fitness evaluations is critical for accelerating convergence and improving algorithmic performance. However, designing an optimization strategy that effectively balances convergence, diversity, and distribution remains a challenge. To tackle this issue, we propose a composite indicator-based evolutionary algorithm (CI-EMO) for expensive multi-objective optimization. In each generation of the optimization process, CI-EMO first employs NSGA-III to explore the solution space based on fitness values predicted by surrogate models, generating a candidate population. Subsequently, we design a novel composite performance indicator to guide the selection of candidates for real fitness evaluation. This indicator simultaneously considers convergence, diversity, and distribution to improve the efficiency of identifying promising candidate solutions, which significantly improves algorithm performance. The composite indicator-based candidate selection strategy is easy to achieve and computes efficiency. Component analysis experiments confirm the effectiveness of each element in the composite performance indicator. Comparative experiments on benchmark problems demonstrate that the proposed algorithm outperforms five state-of-the-art expensive multi-objective optimization algorithms.

MToP: A MATLAB Optimization Platform for Evolutionary Multitasking

Evolutionary multitasking (EMT) has been attracting much attention over the past years. It aims to handle multiple optimization tasks simultaneously within limited computing resources assisted by inter-task knowledge transfer techniques. Numerous multitask evolutionary algorithms (MTEAs) for solving multitask optimization (MTO) problems have been proposed in the EMT field, but there lacks a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications. To address this issue, we introduce the first open-source optimization platform, named MTO-Platform (MToP), for EMT. It incorporates more than 30 MTEAs, more than 150 MTO problem cases with real-world applications, and more than 10 performance metrics. Moreover, for comparing MTEAs with traditional evolutionary algorithms, we modified more than 30 popular single-task evolutionary algorithms to be able to solve MTO problems in MToP. MToP is a user-friendly tool with a graphical user interface that makes it easy to analyze results, export data, and plot schematics. More importantly, MToP is extensible, allowing users to develop new algorithms and define new problems. The source code of MToP is available at https://github.com/intLyc/MTO-Platform.