Wen Shi , Feng-Feng Wei , Xiaolin Bo , Zhisheng Bi , Jianing Xi , Wei-Neng Chen
{"title":"活动属性场景摄动下项目调度的异步风暴规范蚁群系统","authors":"Wen Shi , Feng-Feng Wei , Xiaolin Bo , Zhisheng Bi , Jianing Xi , Wei-Neng Chen","doi":"10.1016/j.swevo.2025.102141","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102141"},"PeriodicalIF":8.5000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Asynchronous storming-norming ant colony system for project scheduling under scenario perturbation of activity attributes\",\"authors\":\"Wen Shi , Feng-Feng Wei , Xiaolin Bo , Zhisheng Bi , Jianing Xi , Wei-Neng Chen\",\"doi\":\"10.1016/j.swevo.2025.102141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":\"98 \",\"pages\":\"Article 102141\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2025-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm and Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210650225002986\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225002986","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.