活动属性场景摄动下项目调度的异步风暴规范蚁群系统

IF 8.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wen Shi , Feng-Feng Wei , Xiaolin Bo , Zhisheng Bi , Jianing Xi , Wei-Neng Chen
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引用次数: 0

摘要

项目日程安排在基础设施和医疗保健等复杂领域至关重要,但目前的方法往往无法捕捉到不同活动属性(如成本和持续时间)之间微妙的相互作用。这些因素充满了不确定性,可能导致不可预测的变化和不可靠的评估。为了缓解这些问题,我们引入了一种基于场景统一模拟的异步风暴规范蚁群系统(ASN-ACS-SU)。首先,我们引入了一个场景统一框架,确保模拟在不同场景下是一致的,从而大大提高了稳定性。接下来,我们整合了风暴规范策略,根据进化阶段调整不同的策略,有效地加速了收敛。最后,采用异步方案,为异步处理各种活动属性量身定制,以提高解决方案的有效性。这些组件被巧妙地集成到蚁群系统框架中,确保了它们各自优势的和谐结合。利用仿真数据集进行的综合测试表明,ASN-ACS-SU显著优于现有算法,包括CH-GA、LRBH、Hybrid DE和两阶段遗传算法,这些算法是解决多模式资源约束项目调度问题的最先进算法。在所有数据集中,所提出的方法在至少90%的场景中优于CH-GA和Hybrid DE,在至少80%的场景中优于两阶段遗传算法,在至少70%的场景中优于LRBH。从而验证了ASN-ACS-SU的有效性和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Asynchronous storming-norming ant colony system for project scheduling under scenario perturbation of activity attributes

Asynchronous storming-norming ant colony system for project scheduling under scenario perturbation of activity attributes
Project scheduling is essential in complex fields like infrastructure and healthcare, but current methods often fail to capture the subtle interplay between different activity attributes, such as cost and duration. These factors, fraught with uncertainty, can lead to unpredictable changes and unreliable assessments. To alleviate these issues, we introduce a novel approach called Asynchronous Storming-Norming Ant Colony System based on Scenario Unification simulation(ASN-ACS-SU). Firstly, we introduce a scenario unification framework, ensuring simulations are consistent across scenarios, thus greatly enhancing stability. Next, we integrate a storming-norming strategy, adapting different tactics based on the evolution stage, effectively accelerating convergence. Lastly, an asynchronous scheme, tailored for handling various activity attributes asynchronously, is incorporated to improve the effectiveness of the solution. These components are skillfully integrated into the Ant Colony System framework, ensuring a harmonious combination of their individual strengths. Comprehensive tests using simulation datasets and demonstrate that ASN-ACS-SU significantly outperforms existing algorithms, including CH-GA, LRBH, Hybrid DE and Two-stage GA, which are state-of-the-art algorithms for multi-mode resource-constrained project scheduling problems. The proposed method demonstrates non-inferiority to CH-GA and Hybrid DE in at least 90% of scenarios, to Two-stage GA in at least 80% of scenarios and to LRBH in at least 70% of scenarios in all datasets. Thus, the validity and reliability of the ASN-ACS-SU can be demonstrated.
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来源期刊
Swarm and Evolutionary Computation
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.
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