A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization

• URL: http://arxiv.org/abs/2604.01241v1
• Date: Mon, 30 Mar 2026 00:18:05 GMT
• Title: A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization
• Abstract summary: We propose the Learning-Based Heterogeneous Cooperative Coevolution Framework (LH-CC)<n>LH-CC adaptively selects the most suitable subproblems for each H-LSGO problem instances.<n>Our findings reveal that dynamic selection is a pivotal strategy for solving complex H-LSGO problems.
• Score: 21.998622908002464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cooperative Coevolution (CC) effectively addresses Large-Scale Global Optimization (LSGO) via decomposition but struggles with the emerging class of Heterogeneous LSGO (H-LSGO) problems arising from real-world applications, where subproblems exhibit diverse dimensions and distinct landscapes. The prevailing CC paradigm, relying on a fixed low-dimensional optimizer, often fails to navigate this heterogeneity. To address this limitation, we propose the Learning-Based Heterogeneous Cooperative Coevolution Framework (LH-CC). By formulating the optimization process as a Markov Decision Process, LH-CC employs a meta-agent to adaptively select the most suitable optimizer for each subproblem. We also introduce a flexible benchmark suite to generate diverse H-LSGO problem instances. Extensive experiments on 3000-dimensional problems with complex coupling relationships demonstrate that LH-CC achieves superior solution quality and computational efficiency compared to state-of-the-art baselines. Furthermore, the framework exhibits robust generalization across varying problem instances, optimization horizons, and optimizers. Our findings reveal that dynamic optimizer selection is a pivotal strategy for solving complex H-LSGO problems.

Related papers

• Hybrid Augmented Lagrangian Method for General Constrained Optimization via Evolutionary Algorithms [0.0]
  We propose a Hybrid Augmented Lagrangian (HyAL) method that integrates the AL framework's constraint-handling strengths with the exploratory power of population-based search.<n>Our approach employs evolutionary techniques to solve subproblems within the AL iterations, promoting exploration and aiding in the escape from local optima.<n>Our results show that HyAL consistently produces high-quality solutions across the benchmark suite.
  arXiv  Detail & Related papers  (2026-07-18T16:29:06Z)
• NCO4CVRP: Neural Combinatorial Optimization for the Capacitated Vehicle Routing Problem [0.23560550919269374]
  This work focuses on improving existing inference techniques to enhance solution quality and generalization.<n>By refining inference techniques and leveraging enhanced search strategies, our work contributes to the broader applicability of NCO models in real-world optimization tasks.
  arXiv  Detail & Related papers  (2026-04-17T14:28:50Z)
• AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization [61.535567824938205]
  We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem.<n>AdaEvolve consistently outperforms the open-ended baselines across 185 different open-ended optimization problems.
  arXiv  Detail & Related papers  (2026-02-23T18:45:31Z)
• LLM4CMO: Large Language Model-aided Algorithm Design for Constrained Multiobjective Optimization [54.35609820607923]
  Large language models (LLMs) offer new opportunities for assisting with algorithm design.<n>We propose LLM4CMO, a novel CMOEA based on a dual-population, two-stage framework.<n>LLMs can serve as efficient co-designers in the development of complex evolutionary optimization algorithms.
  arXiv  Detail & Related papers  (2025-08-16T02:00:57Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization [12.899626317088885]
  This paper introduces LCC, a pioneering learning-based cooperative coevolution framework.<n> LCC dynamically schedules decomposition strategies during optimization processes.<n>It offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption.
  arXiv  Detail & Related papers  (2025-04-24T14:09:22Z)
• A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping [8.839347987566336]
  We propose a novel two-phase cooperative co-evolution framework to address large-scale global optimization problems with complex overlapping.<n>An effective method for decomposing overlapping problems, grounded in their mathematical properties, is embedded within the framework.<n>Extensive experiments demonstrate that the algorithm instantiated within our framework significantly outperforms existing algorithms.
  arXiv  Detail & Related papers  (2025-03-23T13:21:11Z)
• Make Optimization Once and for All with Fine-grained Guidance [78.14885351827232]
  Learning to Optimize (L2O) enhances optimization efficiency with integrated neural networks.<n>L2O paradigms achieve great outcomes, e.g., refitting, generating unseen solutions iteratively or directly.<n>Our analyses explore general framework for learning optimization, called Diff-L2O, focusing on augmenting solutions from a wider view.
  arXiv  Detail & Related papers  (2025-03-14T14:48:12Z)
• Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization [15.476478159958416]
  We employ a large language model (LLM) to enhance evolutionary search for solving constrained multi-objective optimization problems. Our aim is to speed up the convergence of the evolutionary population.
  arXiv  Detail & Related papers  (2024-05-09T13:44:04Z)
• A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization [53.14532968909759]
  We introduce an efficient single-loop primal-dual block-coordinate algorithm called ALEXR.<n>We establish the convergence rates of ALEXR in both convex and strongly convex cases under smoothness and non-smoothness conditions.<n> Experimental results on GDRO and partial Area Under the ROC Curve for cFCCO demonstrate the promising performance of our algorithm.
  arXiv  Detail & Related papers  (2023-12-04T19:00:07Z)
• Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets [86.43523688236077]
  Combinatorial optimization (CO) problems are often NP-hard and out of reach for exact algorithms. GFlowNets have emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially. In this paper, we design Markov decision processes (MDPs) for different problems and propose to train conditional GFlowNets to sample from the solution space.
  arXiv  Detail & Related papers  (2023-05-26T15:13:09Z)
• VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making [12.361459296815559]
  Design strategy of hybrid flower pollination algorithm for Virtual Network Embedding (VNE) problem is discussed. Cross operation is used to replace the cross-pollination operation to complete the global search. Life cycle mechanism is introduced as a complement to the traditional fitness-based selection strategy.
  arXiv  Detail & Related papers  (2022-02-07T00:57:00Z)
• A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs [91.07247251502564]
  We propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph. Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity.
  arXiv  Detail & Related papers  (2021-06-09T09:18:18Z)
• Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks [12.18471608552718]
  Large-scale multiobjective optimization problems (LSMOPs) are characterized as involving hundreds or even thousands of decision variables and multiple conflicting objectives. Previous research has shown that these optimal solutions are uniformly distributed on the manifold structure in the low-dimensional space. In this work, a generative adversarial network (GAN)-based manifold framework is proposed to learn the manifold and generate high-quality solutions.
  arXiv  Detail & Related papers  (2021-01-08T09:38:38Z)
• 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)

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.