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 Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view.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
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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 this 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 remove unimportant attributes. Ignoring filtered…
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Graphic Abstract
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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 this 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. This paper reviews the concepts,…
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Graphic Abstract
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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 this 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 change dynamically over time. Secondly,…
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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 this 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 the physical properties of
the…
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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 this 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 results show
that the average…
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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 this 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 trapped in the local
optimum,…
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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 this 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 efficiently. The experiments show…
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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 this 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 the diversity of the population…
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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 this 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’ features. In the experimental results,…
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