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  • Open Access


    A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

    Chuan Wang1,*, Ruoyu Zhu2, Yi Jiang3, Weili Liu4, Sang-Woon Jeon5, Lin Sun2, Hua Wang6

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1209-1228, 2023, DOI:10.32604/cmes.2022.022807

    Abstract The dynamic traveling salesman problem (DTSP) is significant in logistics distribution in real-world applications in smart cities, but it is uncertain and difficult to solve. This paper proposes a scheme library-based ant colony optimization (ACO) with a two-optimization (2-opt) strategy to solve the DTSP efficiently. The work is novel and contributes to three aspects: problem model, optimization framework, and algorithm design. Firstly, in the problem model, traditional DTSP models often consider the change of travel distance between two nodes over time, while this paper focuses on a special DTSP model in that the node locations change dynamically over time. Secondly,… More >

  • Open Access


    Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine

    S. Keerthi1,*, P. Santhi2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1173-1188, 2023, DOI:10.32604/iasc.2023.029959

    Abstract The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors. The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant. Most tumors are misdiagnosed due to the variability and complexity of lesions, which reduces the survival rate in patients. Diagnosis of brain tumors via computer vision algorithms is a challenging task. Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures. Traditional brain tumor identification techniques require manual segmentation or handcrafted… More >

  • Open Access


    An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization

    Lina M. K. Al-Ebbini*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1971-1985, 2023, DOI:10.32604/iasc.2023.030100

    Abstract A relationship between lung transplant success and many features of recipients’/donors has long been studied. However, modeling a robust model of a potential impact on organ transplant success has proved challenging. In this study, a hybrid feature selection model was developed based on ant colony optimization (ACO) and k-nearest neighbor (kNN) classifier to investigate the relationship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing (UNOS). The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’ functional status (i.e., quality of life) after… More >

  • Open Access


    An Ant Colony Optimization Algorithm for Stacking Sequence Design of Composite Laminates

    F. Aymerich1, M. Serra2

    CMES-Computer Modeling in Engineering & Sciences, Vol.13, No.1, pp. 49-66, 2006, DOI:10.3970/cmes.2006.013.049

    Abstract The study reported in this paper explores the potential of Ant Colony Optimization (ACO) metaheuristic for stacking sequence optimization of composite laminates. ACO is a recently proposed population-based search approach able to deal with a wide range of optimization problems, especially of a combinatorial nature, and inspired by the natural foraging behavior of ant colonies. ACO search processes, in which the activities of real ants are simulated by means of artificial agents that communicate and cooperate through the modification of the local environment, were implemented in a specifically developed numerical algorithm aimed at the lay-up optimization (based on a strain… More >

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