Qiang Luo , Chunrong Pan , Hong Zhong , Yunqing Rao
{"title":"A decimal artificial bee colony with elite strategy for the cutting stock problem with irregular items","authors":"Qiang Luo , Chunrong Pan , Hong Zhong , Yunqing Rao","doi":"10.1016/j.swevo.2025.102026","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates an irregular cutting stock problem in various industrial applications, including shipbuilding, construction machinery, and automobiles, where a considerable quantity of metal sheets are consumed. The problem involves cutting the single-size stocks to produce a set of demanded items such that the material utilization is maximized, i.e., the waste is minimized. To address the problem, this study employs the double scanline to represent the irregular items, and proposes a decimal artificial bee colony with elite strategy. The algorithm represents solutions with decimal vectors and uses a decoder procedure to map these vectors to solutions of the problem. In addition, a metaheuristic-based hybrid algorithm is developed for further improving the solution quality. To comprehensively assess the performance of the algorithm, two sets of computational tests were conducted. The experimental results demonstrated that the proposed algorithm outperforms competing algorithms by achieving faster convergence than other metaheuristics of the same class and producing better solutions, verifying the algorithm's effectiveness and superiority. The implementation of the algorithm benefits waste reduction for companies in practice.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102026"},"PeriodicalIF":8.2000,"publicationDate":"2025-07-03","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/S2210650225001841","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}
引用次数: 0
Abstract
This study investigates an irregular cutting stock problem in various industrial applications, including shipbuilding, construction machinery, and automobiles, where a considerable quantity of metal sheets are consumed. The problem involves cutting the single-size stocks to produce a set of demanded items such that the material utilization is maximized, i.e., the waste is minimized. To address the problem, this study employs the double scanline to represent the irregular items, and proposes a decimal artificial bee colony with elite strategy. The algorithm represents solutions with decimal vectors and uses a decoder procedure to map these vectors to solutions of the problem. In addition, a metaheuristic-based hybrid algorithm is developed for further improving the solution quality. To comprehensively assess the performance of the algorithm, two sets of computational tests were conducted. The experimental results demonstrated that the proposed algorithm outperforms competing algorithms by achieving faster convergence than other metaheuristics of the same class and producing better solutions, verifying the algorithm's effectiveness and superiority. The implementation of the algorithm benefits waste reduction for companies in practice.
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.