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The Development and Application of Swarm Intelligence
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The Development and Application of Swarm Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 11373

Special Issue Editor

Dr. Nor Azlina Ab. Aziz

E-Mail Website
Guest Editor
Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia
Interests: swarm intelligence; metaheuristics; bioinspired algorithm

Special Issue Information

Dear Colleagues,

Swarm intelligence (SI) is inspired by collective behavior observed in nature. Similarly to the collective behavior of many simple beings, which use experience and information sharing to guide members of the swarm in achieving a complex objective, this Special Issue has a similar goal. The concept of SI dominates the field of metaheuristics. Among state-of-the-art SI algorithms are particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and many others. These algorithms work like black boxes, where the same algorithm can be applied for various optimization problems. Indeed, the algorithms have been successfully applied in many problems across various fields, such as communication problems, logistic, engineering design, material research, bioinformatics, software, financial, and operational research. Despite the success of SI, however, there is room for improvement in this field. SI algorithms often face the issue of balancing exploration and exploitation as well as premature convergence. Moreover, the no free lunch theorem states that no single ultimate algorithm exists that works best for all types of optimization problems. Thus, researchers must continue improving the existing SI algorithms and proposing new ones.

This Special Issue invites original research and review articles that look into new applications of SI and the development of new or improved SI algorithms.

Dr. Nor Azlina Ab. Aziz
Guest Editor

Manuscript Submission Information

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Keywords

  • swarm intelligence
  • bioinspired algorithm
  • metaheuristic algorithm
  • optimization
  • particle swarm optimization
  • ant colony optimization
  • artificial bee colony

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Published Papers (5 papers)

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Research

19 pages, 1057 KiB  
Article
Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
by Muhammad Nadzree Mohd Yamin, Kamarulzaman Ab. Aziz, Tan Gek Siang and Nor Azlina Ab. Aziz
Appl. Sci. 2023, 13(12), 7054; https://doi.org/10.3390/app13127054 - 12 Jun 2023
Cited by 5 | Viewed by 1617
Abstract
Particle Swarm Optimisation (PSO) is a popular technique in the field of Swarm Intelligence (SI) that focuses on optimisation. Researchers have explored multiple algorithms and applications of PSO, including exciting new technologies, such as Emotion Recognition Systems (ERS), which enable computers or machines [...] Read more.
Particle Swarm Optimisation (PSO) is a popular technique in the field of Swarm Intelligence (SI) that focuses on optimisation. Researchers have explored multiple algorithms and applications of PSO, including exciting new technologies, such as Emotion Recognition Systems (ERS), which enable computers or machines to understand human emotions. This paper aims to review previous studies related to PSO findings for ERS and identify modalities that can be used to achieve better results through PSO. To achieve a comprehensive understanding of previous studies, this paper will adopt a Systematic Literature Review (SLR) process to filter related studies and examine papers that contribute to the field of PSO in ERS. The paper’s primary objective is to provide better insights into previous studies on PSO algorithms and techniques, which can help future researchers develop more accurate and sustainable ERS technologies. By analysing previous studies over the past decade, the paper aims to identify gaps and limitations in the current research and suggest potential areas for future research. Overall, this paper’s contribution is twofold: first, it provides an overview of the use of PSO in ERS and its potential applications. Second, it offers insights into the contributions and limitations of previous studies and suggests avenues for future research. This can lead to the development of more effective and sustainable ERS technologies, with potential applications in a wide range of fields, including healthcare, gaming, and customer service. Full article
(This article belongs to the Special Issue The Development and Application of Swarm Intelligence)
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24 pages, 2691 KiB  
Article
An Advanced Crow Search Algorithm for Solving Global Optimization Problem
by Donwoo Lee, Jeonghyun Kim, Sudeok Shon and Seungjae Lee
Appl. Sci. 2023, 13(11), 6628; https://doi.org/10.3390/app13116628 - 30 May 2023
Cited by 5 | Viewed by 1524
Abstract
The conventional crow search (CS) algorithm is a swarm-based metaheuristic algorithm that has fewer parameters, is easy to apply to problems, and is utilized in various fields. However, it has a disadvantage, as it is easy for it to fall into local minima [...] Read more.
The conventional crow search (CS) algorithm is a swarm-based metaheuristic algorithm that has fewer parameters, is easy to apply to problems, and is utilized in various fields. However, it has a disadvantage, as it is easy for it to fall into local minima by relying mainly on exploitation to find approximations. Therefore, in this paper, we propose the advanced crow search (ACS) algorithm, which improves the conventional CS algorithm and solves the global optimization problem. The ACS algorithm has three differences from the conventional CS algorithm. First, we propose using dynamic AP (awareness probability) to perform exploration of the global region for the selection of the initial population. Second, we improved the exploitation performance by introducing a formula that probabilistically selects the best crows instead of randomly selecting them. Third, we improved the exploration phase by adding an equation for local search. The ACS algorithm proposed in this paper has improved exploitation and exploration performance over other metaheuristic algorithms in both unimodal and multimodal benchmark functions, and it found the most optimal solutions in five engineering problems. Full article
(This article belongs to the Special Issue The Development and Application of Swarm Intelligence)
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36 pages, 12925 KiB  
Article
Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
by Khaled Mohammad Alhashash, Hussein Samma and Shahrel Azmin Suandi
Appl. Sci. 2023, 13(8), 5102; https://doi.org/10.3390/app13085102 - 19 Apr 2023
Cited by 1 | Viewed by 2349
Abstract
There are many pre-trained deep learning-based face recognition models developed in the literature, such as FaceNet, ArcFace, VGG-Face, and DeepFace. However, performing transfer learning of these models for handling face sketch recognition is not applicable due to the challenge of limited sketch datasets [...] Read more.
There are many pre-trained deep learning-based face recognition models developed in the literature, such as FaceNet, ArcFace, VGG-Face, and DeepFace. However, performing transfer learning of these models for handling face sketch recognition is not applicable due to the challenge of limited sketch datasets (single sketch per subject). One promising solution to mitigate this issue is by using optimization algorithms, which will perform a fine-tuning and fitting of these models for the face sketch problem. Specifically, this research introduces an enhanced optimizer that will evolve these models by performing automatic weightage/fine-tuning of the generated feature vector guided by the recognition accuracy of the training data. The following are the key contributions to this work: (i) this paper introduces a novel Smart Switching Slime Mold Algorithm (S2SMA), which has been improved by embedding several search operations and control rules; (ii) the proposed S2SMA aims to fine-tune the pre-trained deep learning models in order to improve the accuracy of the face sketch recognition problem; and (iii) the proposed S2SMA makes simultaneous fine-tuning of multiple pre-trained deep learning models toward further improving the recognition accuracy of the face sketch problem. The performance of the S2SMA has been evaluated on two face sketch databases, which are XM2VTS and CUFSF, and on CEC’s 2010 large-scale benchmark. In addition, the outcomes were compared to several variations of the SMA and related optimization techniques. The numerical results demonstrated that the improved optimizer obtained a higher level of fitness value as well as better face sketch recognition accuracy. The statistical data demonstrate that S2SMA significantly outperforms other optimization techniques with a rapid convergence curve. Full article
(This article belongs to the Special Issue The Development and Application of Swarm Intelligence)
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17 pages, 1368 KiB  
Article
Improvement Technique for Group Search Optimization Using Experimental Design Method
by Po-Yuan Yang, Kai-Yu Yang, Wen-Hsien Ho, Fu-I Chou and Jyh-Horng Chou
Appl. Sci. 2023, 13(5), 3205; https://doi.org/10.3390/app13053205 - 2 Mar 2023
Cited by 1 | Viewed by 1477
Abstract
This study proposes the use of an experimental design approach in GSO, and a systematic approach to deal with the hyperparameter settings of GSOs and to provide stable algorithmic performance of GSOs through the experimental design approach. To address these two issues, this [...] Read more.
This study proposes the use of an experimental design approach in GSO, and a systematic approach to deal with the hyperparameter settings of GSOs and to provide stable algorithmic performance of GSOs through the experimental design approach. To address these two issues, this study explores the combination of hyperparameters that can improve the performance of GSOs using a uniform design. In addition, the Taguchi method and optimal operations were used to derive an excellent combination of parameters that would provide the best value and robustness of the function to provide a stable performance of GSO. The validity of the performance of the proposed method was tested using ten benchmark functions, including three unimodal, three multimodal, and four restricted multimodal functions. The results were compared with the t-distribution test in addition to the mean and standard deviation to analyze their validity. The results of the t-distribution test showed that the p-values obtained for both UD-GSO and R-GSO were less than 0.05, indicating significant differences compared with GSO for both unimodal and multimodal functions. Two restricted multimodal functions are not significantly different, while the other two are below 0.05, indicating significant differences. This shows that the performance obtained using UD-GSO and R-GSO is more effective than the original GSO. UD-GSO and R-GSO provide better and more robust results than GSO. The main contributions of this paper are as follows: (i) This study proposes a uniform design approach to overcome the difficulties of setting hyperparameters in GSO. (ii) This study proposes a Taguchi method and optimal operation to provide a robust calculation for GSO. (iii) The method applied in this study provides systematic parameter design to solve GSO parameter setting and robust result obtaining. Full article
(This article belongs to the Special Issue The Development and Application of Swarm Intelligence)
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29 pages, 8466 KiB  
Article
Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems
by Muhammad Kamran Khan, Muhammad Hamza Zafar, Saad Rashid, Majad Mansoor, Syed Kumayl Raza Moosavi and Filippo Sanfilippo
Appl. Sci. 2023, 13(2), 945; https://doi.org/10.3390/app13020945 - 10 Jan 2023
Cited by 20 | Viewed by 3521
Abstract
The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. [...] Read more.
The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique. Full article
(This article belongs to the Special Issue The Development and Application of Swarm Intelligence)
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