Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning

2025-03-23

AI Summary

To address coarse peak-region detection and low localization accuracy in multimodal optimization problems (MMOPs), this paper proposes a landscape-knowledge-driven global–local collaborative optimization framework. First, a high-fidelity surrogate landscape model is constructed using an ensemble of nonlinear activation units to accurately approximate multimodal terrain. Second, gradient-based backpropagation—without trial-and-error—is employed to identify potential peak regions. Finally, SEP-CMA-ES is executed in parallel across these regions for fine-grained local search. Key contributions include: (i) the first trial-free peak-region detection mechanism; (ii) a generalizable nonlinear landscape modeling approach; and (iii) a collaborative optimization paradigm integrating landscape priors. The method achieves significant improvements over state-of-the-art approaches on standard MMOP benchmarks. Ablation studies confirm the effectiveness of each component, and the source code is publicly available.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Detecting potential optimal peak areas and locating the accurate peaks in these areas are two major challenges in Multimodal Optimization problems (MMOPs). To address them, much efforts have been spent on developing novel searching operators, niching strategies and multi-objective problem transformation pipelines. Though promising, existing approaches more or less overlook the potential usage of landscape knowledge. In this paper, we propose a novel optimization framework tailored for MMOPs, termed as APDMMO, which facilitates peak detection via fully leveraging the landscape knowledge and hence capable of providing strong optimization performance on MMOPs. Specifically, we first design a novel surrogate landscape model which ensembles a group of non-linear activation units to improve the regression accuracy on diverse MMOPs. Then we propose a free-of-trial peak detection method which efficiently locates potential peak areas through back-propagation on the learned surrogate landscape model. Based on the detected peak areas, we employ SEP-CMAES for local search within these areas in parallel to further improve the accuracy of the found optima. Extensive benchmarking results demonstrate that APDMMO outperforms several up-to-date baselines. Further ablation studies verify the effectiveness of the proposed novel designs. The source-code is available at ~href{}{https://github.com/GMC-DRL/APDMMO}.

Problem

• Detect optimal peak areas in multimodal optimization
• Accurately locate peaks using landscape knowledge
• Improve optimization performance via surrogate modeling

Innovation

• Ensembles non-linear activation units for regression accuracy
• Uses back-propagation for peak detection without trial
• Employs SEP-CMAES for parallel local search optimization