Diversity-enhanced hyper-heuristics for multi-objective dynamic flexible job shop scheduling

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-06-02 DOI:10.1016/j.swevo.2025.101994

Yuan Shi , Yaoming Yang , Bingdong Li , Hong Qian , Hao Hao , Aimin Zhou

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引用次数: 0

Abstract

In the realm of multi-objective dynamic flexible job shop scheduling (MODFJSS), the prevalent reliance on genetic programming based hyper-heuristics (GPHH) has been identified as a bottleneck with quality-limited and redundant heuristics. To deal with these issues, this study introduces a novel approach named Diversity-Enhanced Hyper-Heuristics (DEHH). Our methodology encompasses three strategic thrusts: First, we introduce a multi-grained knowledge (MGK) method to represent knowledge more accurately. Second, we propose an explicit knowledge sharing (EKS) mechanism coupled with surrogate models to discern a diverse set of problem-relevant knowledge. Third, we design a multiple Pareto retrieval (MPR) mechanism to curb the proliferation of duplicate heuristics during evolution. Through comprehensive experimentation, we demonstrate that DEHH achieves superior generalization ability and diversity performance across various scenarios compared with state-of-the-art GPHH algorithms.

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多目标动态柔性作业车间调度的多样性增强超启发式算法

在多目标动态柔性作业车间调度（MODFJSS）领域，普遍依赖基于遗传规划的超启发式算法（GPHH）已被认为是启发式算法存在质量限制和冗余的瓶颈。为了解决这些问题，本研究引入了一种名为多样性增强超启发式（DEHH）的新方法。我们的方法包含三个战略重点：首先，我们引入了多粒度知识（MGK）方法来更准确地表示知识。其次，我们提出了一个明确的知识共享（EKS）机制，结合代理模型来识别不同的问题相关知识集。第三，我们设计了一个多重Pareto检索（MPR）机制来抑制进化过程中重复启发式的泛滥。通过综合实验，我们证明了与最先进的GPHH算法相比，DEHH在各种场景下具有更好的泛化能力和多样性性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.