Special Issues
Table of Content

Swarm Intelligence and Applications in Combinatorial Optimization

Submission Deadline: 31 March 2022 (closed) View: 32

Guest Editors

Dr. Gai-Ge Wang, Ocean University of China, Qingdao, China
Dr. Xiao-Zhi Gao, University of Eastern Finland, Finland
Dr. Amir H. Alavi, University of Pittsburgh, USA

Summary

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. In SI, an individual has a simple structure and its function is single. However, such systems composed by many individuals show the phenomenon of emergence and can address several difficult real-world problems that are impossible to be solved by only an individual. During recent decades, SI methods have been successfully applied to cope with complex and time-consuming problems that are hard to be solved by traditional mathematical methods. Therefore, SI is indeed a topic of interest amongst researchers in various fields of science and engineering. Some popular SI paradigms, including ant colony optimization, and particle swarm optimization, have been successfully applied to handle various practical engineering problems.


Combinatorial optimization is a subset of mathematical optimization related to operational research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, mathematics, auction theory, and software engineering. Many real-world problems can be modeled and solved as combinatorial optimization problems. This is an active research area, where new formulations, algorithms, practical applications, and theoretical results are often proposed and published. Current challenges in the field involve modeling of hard problems, development of exact methods, design and experimental evaluation of approximate and hybrid methods, among others.


The overall aim of this special issue is to compile the latest research and development, up-to-date issues, and challenges in the field of SI and its applications in combinatorial optimization. Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view. Potential topics include, but are not only limited to:  


Swarm Intelligence Algorithms

 Improvements of traditional SI methods (e.g., ant colony optimization and particle swarm optimization)

• Recent development of SI methods (e.g., monarch butterfly optimization, earthworm optimization algorithm, elephant herding optimization, moth search algorithm, bird swarm algorithm, chicken swarm optimization, fireworks algorithm, and brain storm optimization)

• Theoretical study on SI algorithms using various techniques (e.g., Markov chain, dynamic system, complex system/networks, and Martingale)


Applications in Combinatorial Optimization

• Scheduling (e.g., vehicle rescheduling, nurse scheduling problem, flow shop scheduling, and fuzzy scheduling)

• Traveling salesman problem (e.g., symmetric traveling salesman problem, asymmetric traveling salesman problem, fuzzy traveling salesman problem, and other real-world problems that can be converted to traveling salesman problem)

• Knapsack problem (e.g., 0/1 knapsack problem, multi-objective knapsack problem, multi-dimensional knapsack problem, multiple knapsack problem, and quadratic knapsack problem)

• Others (e.g., constraint satisfaction problem, set cover problem, task assignment problem, and portfolio optimization)



Published Papers


  • Open Access

    ARTICLE

    Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

    Dan Zhang, Yiwen Liang, Hongbin Dong
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2025-2045, 2023, DOI:10.32604/cmes.2023.022864
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract The artificial immune system, an excellent prototype for developing Machine Learning, is inspired by the function of the powerful natural immune system. As one of the prevalent classifiers, the Dendritic Cell Algorithm (DCA) has been widely used to solve binary problems in the real world. The classification of DCA depends on a data pre-processing procedure to generate input signals, where feature selection and signal categorization are the main work. However, the results of these studies also show that the signal generation of DCA is relatively weak, and all of them utilized a filter strategy to… More >

    Graphic Abstract

    Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

  • Open Access

    REVIEW

    Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

    Qiqi Zhang, Shaojin Geng, Xingjuan Cai
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1863-1900, 2023, DOI:10.32604/cmes.2023.022287
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. More >

    Graphic Abstract

    Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

  • Open Access

    ARTICLE

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

    Chuan Wang, Ruoyu Zhu, Yi Jiang, Weili Liu, Sang-Woon Jeon, Lin Sun, Hua Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1209-1228, 2023, DOI:10.32604/cmes.2022.022807
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    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… More >

  • Open Access

    ARTICLE

    Three-Stages Hyperspectral Image Compression Sensing with Band Selection

    Jingbo Zhang, Yanjun Zhang, Xingjuan Cai, Liping Xie
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 293-316, 2023, DOI:10.32604/cmes.2022.020426
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract Compressed sensing (CS), as an efficient data transmission method, has achieved great success in the field of data transmission such as image, video and text. It can robustly recover signals from fewer Measurements, effectively alleviating the bandwidth pressure during data transmission. However, CS has many shortcomings in the transmission of hyperspectral image (HSI) data. This work aims to consider the application of CS in the transmission of hyperspectral image (HSI) data, and provides a feasible research scheme for CS of HSI data. HSI has rich spectral information and spatial information in bands, which can reflect… More >

  • Open Access

    ARTICLE

    Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet

    Helong Yu, Xianhe Cheng, Ziqing Li, Qi Cai, Chunguang Bi
    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 711-738, 2022, DOI:10.32604/cmes.2022.020263
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient channel attention module (ECANet) to suppress noise from a complex background. The experimental… More >

  • Open Access

    ARTICLE

    An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

    Yaning Xiao, Xue Sun, Yanling Guo, Sanping Li, Yapeng Zhang, Yangwei Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 815-850, 2022, DOI:10.32604/cmes.2022.019198
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting… More >

  • Open Access

    ARTICLE

    Strengthened Initialization of Adaptive Cross-Generation Differential Evolution

    Wei Wan, Gaige Wang, Junyu Dong
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1495-1516, 2022, DOI:10.32604/cmes.2021.017987
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract Adaptive Cross-Generation Differential Evolution (ACGDE) is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms (EAs). However, its convergence and diversity are not satisfactory compared with the latest algorithms. In order to adapt to the current environment, ACGDE requires improvements in many aspects, such as its initialization and mutant operator. In this paper, an enhanced version is proposed, namely SIACGDE. It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor. These improvements make the direction of crossgeneration mutation more clearly and the ability of searching More >

  • Open Access

    ARTICLE

    A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

    Andi Tang, Huan Zhou, Tong Han, Lei Xie
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 331-364, 2022, DOI:10.32604/cmes.2021.017310
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract The sparrow search algorithm (SSA) is a newly proposed meta-heuristic optimization algorithm based on the sparrow foraging principle. Similar to other meta-heuristic algorithms, SSA has problems such as slow convergence speed and difficulty in jumping out of the local optimum. In order to overcome these shortcomings, a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy (CLSSA) is proposed in this paper. Firstly, in order to balance the exploration and exploitation ability of the algorithm, chaotic mapping is introduced to adjust the main parameters of SSA. Secondly, in order to improve… More >

  • Open Access

    ARTICLE

    A Step-Based Deep Learning Approach for Network Intrusion Detection

    Yanyan Zhang, Xiangjin Ran
    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1231-1245, 2021, DOI:10.32604/cmes.2021.016866
    (This article belongs to the Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
    Abstract In the network security field, the network intrusion detection system (NIDS) is considered one of the critical issues in the detection accuracy and missed detection rate. In this paper, a method of two-step network intrusion detection on the basis of GoogLeNet Inception and deep convolutional neural networks (CNNs) models is proposed. The proposed method used the GoogLeNet Inception model to identify the network packets’ binary problem. Subsequently, the characteristics of the packets’ raw data and the traffic features are extracted. The CNNs model is also used to identify the multiclass intrusions by the network packets’ More >

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