Using Multi-modal Large Language Model to Boost Fireworks Algorithm's Ability in Settling Challenging Optimization Tasks

• URL: http://arxiv.org/abs/2511.03137v1
• Date: Wed, 05 Nov 2025 03:01:54 GMT
• Title: Using Multi-modal Large Language Model to Boost Fireworks Algorithm's Ability in Settling Challenging Optimization Tasks
• Authors: Shipeng Cen, Ying Tan,
• Abstract summary: High-dimensionality, black-box nature, and other unfavorable characteristics are challenges posed by optimization problems.<n>In this paper, we propose a novel approach by incorporating multi-modal large language model(MLLM)<n>We focus on two specific tasks: the textittraveling salesman problem (TSP) and textitelectronic automation problem (EDA)
• Score: 2.7320188728052064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As optimization problems grow increasingly complex and diverse, advancements in optimization techniques and paradigm innovations hold significant importance. The challenges posed by optimization problems are primarily manifested in their non-convexity, high-dimensionality, black-box nature, and other unfavorable characteristics. Traditional zero-order or first-order methods, which are often characterized by low efficiency, inaccurate gradient information, and insufficient utilization of optimization information, are ill-equipped to address these challenges effectively. In recent years, the rapid development of large language models (LLM) has led to substantial improvements in their language understanding and code generation capabilities. Consequently, the design of optimization algorithms leveraging large language models has garnered increasing attention from researchers. In this study, we choose the fireworks algorithm(FWA) as the basic optimizer and propose a novel approach to assist the design of the FWA by incorporating multi-modal large language model(MLLM). To put it simply, we propose the concept of Critical Part(CP), which extends FWA to complex high-dimensional tasks, and further utilizes the information in the optimization process with the help of the multi-modal characteristics of large language models. We focus on two specific tasks: the \textit{traveling salesman problem }(TSP) and \textit{electronic design automation problem} (EDA). The experimental results show that FWAs generated under our new framework have achieved or surpassed SOTA results on many problem instances.

Related papers

• Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design [124.54166764570972]
  Combinatorial optimization problems are traditionally tackled with handcrafted algorithms.<n>Recent progress has highlighted the potential of automatics design powered by large language models.<n>We propose the Experience-Evolution Reflective Co-Guided of Prompt and Heuristics (EvoPH) for automatic algorithm design.
  arXiv  Detail & Related papers  (2025-09-29T09:24:09Z)
• Large Language Model Assisted Automated Algorithm Generation and Evolution via Meta-black-box optimization [9.184788298623062]
  AwesomeDE is proposed that leverages large language models (LLMs) as the strategy of meta-optimizer to generate update rules for constrained evolutionary algorithm without human intervention.<n>Key components, including prompt design and iterative refinement, are systematically analyzed to determine their impact on design quality.<n> Experimental results demonstrate that the proposed approach outperforms existing methods in terms of computational efficiency and solution accuracy.
  arXiv  Detail & Related papers  (2025-09-16T17:02:24Z)
• Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning [43.63419208391747]
  We present a novel framework that leverages expert-level optimization modeling principles through chain-of-thought reasoning to automate the optimization process.<n>We also introduce LogiOR, a new optimization modeling benchmark from the logistics domain, containing more complex problems with standardized annotations.
  arXiv  Detail & Related papers  (2025-08-20T04:14:54Z)
• Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems [0.0]
  Combinatorial optimization problems often rely on algorithms to generate efficient solutions.<n>Recent advances in artificial intelligence have demonstrated the potential to automate generation through evolutionary frameworks.<n>We propose the Contextual Evolution of Heuristics framework, which incorporates problem-specific descriptions to enhance in-context learning.
  arXiv  Detail & Related papers  (2025-03-05T10:22:49Z)
• Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training [3.668740611399284]
  Large-scale language models (LLM) have achieved remarkable results in a variety of tasks.<n>This paper proposes an improved method based on adaptive optimization algorithm.
  arXiv  Detail & Related papers  (2024-12-06T02:17:30Z)
• M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning [90.75075886543404]
  Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains. In this work, we introduce a novel Multimodal Prompt Tuning (M$2$PT) approach for efficient instruction tuning of MLLMs.
  arXiv  Detail & Related papers  (2024-09-24T01:40:24Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• Reinforced In-Context Black-Box Optimization [64.25546325063272]
  RIBBO is a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion. RIBBO employs expressive sequence models to learn the optimization histories produced by multiple behavior algorithms and tasks. Central to our method is to augment the optimization histories with textitregret-to-go tokens, which are designed to represent the performance of an algorithm based on cumulative regret over the future part of the histories.
  arXiv  Detail & Related papers  (2024-02-27T11:32:14Z)
• SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization [8.975505323004427]
  We propose a novel Cohesive In-Context Prompt Optimization framework for Large Language Models (LLMs)<n>We introduce SEE, a scalable and efficient prompt optimization framework that adopts metaheuristic optimization principles and strategically exploration and exploitation.<n> SEE significantly outperforms state-of-the-art baseline methods by a large margin, achieving an average performance gain of 13.94 while reducing computational costs by 58.67.
  arXiv  Detail & Related papers  (2024-02-17T17:47:10Z)
• Language Models for Business Optimisation with a Real World Case Study in Production Scheduling [3.224702011999591]
  Large Language Models (LLMs) have demonstrated outstanding performance across different language-related tasks.<n>We present an LLM-based framework for automating problem formulation in business optimisation.
  arXiv  Detail & Related papers  (2023-09-22T23:45:21Z)
• Large Language Models as Optimizers [106.52386531624532]
  We propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as prompts. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values. We demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
  arXiv  Detail & Related papers  (2023-09-07T00:07:15Z)
• Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation [101.22379613810881]
  We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process. We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
  arXiv  Detail & Related papers  (2021-02-16T06:04:27Z)
• Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models [63.92643612630657]
  This paper attempts to peek into the black-box of multilingual optimization through the lens of loss function geometry. We find that gradient similarity measured along the optimization trajectory is an important signal, which correlates well with language proximity. We derive a simple and scalable optimization procedure, named Gradient Vaccine, which encourages more geometrically aligned parameter updates for close tasks.
  arXiv  Detail & Related papers  (2020-10-12T17:26:34Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)

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.