Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm

• URL: http://arxiv.org/abs/2512.18947v1
• Date: Mon, 22 Dec 2025 01:51:26 GMT
• Title: Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm
• Authors: Li Yan, Bolun Liu, Chao Li, Jing Liang, Kunjie Yu, Caitong Yue, Xuzhao Chai, Boyang Qu,
• Abstract summary: We introduce a new benchmark suite of dynamic multimodal multiobjective test functions constructed by fusing the properties of both dynamic and multimodal optimization.<n>We propose a novel algorithm centered on a Clustering-based Autoencoder prediction dynamic response mechanism.<n>Our algorithm not only preserves population diversity more effectively in the decision space but also achieves superior convergence in the objective space.
• Score: 6.287677513860692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining population diversity in time-varying environments. However, existing dynamic multiobjective evolutionary algorithms often neglect solution modality, whereas static multimodal multiobjective evolutionary algorithms lack adaptability to dynamic changes. To address above challenge, this paper makes two primary contributions. First, we introduce a new benchmark suite of dynamic multimodal multiobjective test functions constructed by fusing the properties of both dynamic and multimodal optimization to establish a rigorous evaluation platform. Second, we propose a novel algorithm centered on a Clustering-based Autoencoder prediction dynamic response mechanism, which utilizes an autoencoder model to process matched clusters to generate a highly diverse initial population. Furthermore, to balance the algorithm's convergence and diversity, we integrate an adaptive niching strategy into the static optimizer. Empirical analysis on 12 instances of dynamic multimodal multiobjective test functions reveals that, compared with several state-of-the-art dynamic multiobjective evolutionary algorithms and multimodal multiobjective evolutionary algorithms, our algorithm not only preserves population diversity more effectively in the decision space but also achieves superior convergence in the objective space.

Related papers

• Improving Multimodal Sentiment Analysis via Modality Optimization and Dynamic Primary Modality Selection [54.10252086842123]
  Multimodal Sentiment Analysis (MSA) aims to predict sentiment from language, acoustic, and visual data in videos.<n>This paper proposes a modality optimization and dynamic primary modality selection framework (MODS)<n>Experiments on four benchmark datasets demonstrate that MODS outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-09T11:13:32Z)
• Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization [5.481047026874548]
  This paper presents a novel graph neural network (GNN) based DRL to configure multi-objective evolutionary algorithms.<n>We model the dynamic algorithm configuration as a Markov decision process, representing the convergence of solutions in the objective space by a graph.<n> Experiments on diverse MOCO challenges indicate that our method outperforms traditional and DRL-based algorithm configuration methods in terms of efficacy and adaptability.
  arXiv  Detail & Related papers  (2025-05-22T09:53:54Z)
• Layer-wise Quantization for Quantized Optimistic Dual Averaging [75.4148236967503]
  We develop a general layer-wise quantization framework with tight variance and code-length bounds, adapting to the heterogeneities over the course of training.<n>We propose a novel Quantized Optimistic Dual Averaging (QODA) algorithm with adaptive learning rates, which achieves competitive convergence rates for monotone VIs.
  arXiv  Detail & Related papers  (2025-05-20T13:53:58Z)
• Modified CMA-ES Algorithm for Multi-Modal Optimization: Incorporating Niching Strategies and Dynamic Adaptation Mechanism [0.03495246564946555]
  This study modifies the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm for multi-modal optimization problems. The enhancements focus on addressing the challenges of multiple global minima, improving the algorithm's ability to maintain diversity and explore complex fitness landscapes. We incorporate niching strategies and dynamic adaptation mechanisms to refine the algorithm's performance in identifying and optimizing multiple global optima.
  arXiv  Detail & Related papers  (2024-07-01T03:41:39Z)
• Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem [9.617143859697322]
  We explore the use of 3-objective evolutionary algorithms for the chance constrained knapsack problem with dynamic constraints. We introduce a 3-objective formulation that is able to deal with the dynamic components at the same time and is independent of the confidence level required for the constraint. Our analysis highlights the advantages of the 3-objective formulation over the 2-objective formulation in addressing the dynamic chance constrained knapsack problem.
  arXiv  Detail & Related papers  (2024-04-09T04:47:01Z)
• Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance [47.42067405054353]
  Multi-objective learning (MOL) problems often arise in emerging machine learning problems. One of the critical challenges in MOL is the potential conflict among different objectives during the iterative optimization process. Recent works have developed various dynamic weighting algorithms for MOL such as MGDA and its variants.
  arXiv  Detail & Related papers  (2023-05-31T17:31:56Z)
• Multi-Objective GFlowNets [59.16787189214784]
  We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse optimal solutions, based on GFlowNets.
  arXiv  Detail & Related papers  (2022-10-23T16:15:36Z)
• A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences [55.25331349436895]
  Deep generative models have emerged as a popular machine learning-based approach for inverse problems in the life sciences. These problems often require sampling new designs that satisfy multiple properties of interest in addition to learning the data distribution.
  arXiv  Detail & Related papers  (2022-10-19T19:04:45Z)
• Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems [4.859986264602551]
  This paper focuses on the simulation experiments for parameters of DMOPs. A baseline schema for dynamic algorithm evaluation is introduced. We can establish the minimum capability required of purpose-built dynamic algorithms to be useful.
  arXiv  Detail & Related papers  (2022-04-08T15:50:17Z)
• Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees [94.72461292387146]
  We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA) We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$. When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
  arXiv  Detail & Related papers  (2021-10-18T14:00:22Z)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)
• Single- and Multi-Objective Evolutionary Algorithms for the Knapsack Problem with Dynamically Changing Constraints [13.896724650508087]
  We investigate single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios.
  arXiv  Detail & Related papers  (2020-04-27T03:50:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.