Special Issue "Swarm Intelligence and Applications in Combinatorial Optimization"

Submission Deadline: 31 March 2022 (closed)
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


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 Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view.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
  • Three-stages Hyperspectral Image Compression Sensing with Band Selection
  • 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 the physical properties of the… More
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  • Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet
  • 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 results show that the average… More
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  • Strengthened Initialization of Adaptive Cross-Generation Differential Evolution
  • 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 efficiently. The experiments show… More
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  • A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems
  • 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 the diversity of the population… More
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  • A Step-Based Deep Learning Approach for Network Intrusion Detection
  • 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’ features. In the experimental results,… More
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