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Biomimetics
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Advances in Swarm Intelligence Optimization Algorithms and Applications
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Advances in Swarm Intelligence Optimization Algorithms and Applications

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 4909

Special Issue Editors

Prof. Dr. Heming Jia

E-Mail Website
Guest Editor
School of Information Engineering, Sanming University, Sanming, China
Interests: Remora Optimization Algorithm (ROA); Crayfish Optimization Algorithm (COA); Catch Fish Optimization Algorithm (CFOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gang Hu

E-Mail Website
Guest Editor
Department of Applied Mathematics, Xi’an University of Technology, Xi’an, China
Interests: metaheuristic algorithms; computing intelligence; artificial intelligence; complex optimization systems; CAD/CAM; image processing and analysis; path planning; multilevel image segmentation; feature selection; Genghis Khan Shark Optimizer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As industrialization continues to progress at an unprecedented pace, engineering applications are proliferating, accompanied by a myriad of intricate and diverse challenges. To navigate through these complex real-world problems, a plethora of optimization algorithms have been devised, with swarm intelligence optimization algorithms (SIOAs) occupying a prominent position. SIOAs, drawing inspiration from the collective behaviors exhibited by swarms of insects, animals, or other organisms, have demonstrated remarkable abilities in solving non-convex, nonlinearly constrained, and high-dimensional optimization tasks. Their inherent capability to swiftly converge towards optimal solutions while effectively escaping local optima has been well documented in numerous studies.

The Special Issue "Advances in Swarm Intelligence Optimization Algorithms and Applications" aims to consolidate and showcase the latest breakthroughs and achievements in this burgeoning field. It serves as a platform for interdisciplinary research, fostering collaboration among scholars from diverse backgrounds who are exploring the potential of SIOAs for engineering applications. We invite researchers to submit their original contributions that delve into the theoretical foundations, algorithmic innovations, and practical applications of SIOAs, with a focus on addressing specific challenges and advancing the state-of-the-art.

The scope of this Special Issue encompasses, but is not limited to, the following topics:

Novel SIOAs: The development of new swarm intelligence optimization algorithms, including those inspired by unique swarm behaviors or innovative mechanisms for enhancing exploration, exploitation, and convergence.

Hybridization and Integration: Studies exploring the integration of SIOAs with other optimization techniques, machine learning algorithms, or heuristic methods to create hybrid optimization frameworks that leverage the strengths of each approach.

Theoretical Analysis: In-depth analyses of the mathematical properties, convergence behavior, and complexity of SIOAs, providing insights into their performance and limitations.

Parameter Tuning and Adaptation: Research on adaptive parameter control strategies for SIOAs, aimed at enhancing their robustness, versatility, and performance across different problem domains.

High-Dimensional and Complex Problems: Applications of SIOAs to tackle high-dimensional, multimodal, dynamic, and noisy optimization problems, demonstrating their effectiveness in real-world contexts.

Benchmarking and Comparative Studies: Comparative evaluations of SIOAs using standard and novel benchmark functions, highlighting their strengths and weaknesses relative to other optimization techniques.

Engineering Applications: Case studies showcasing the successful application of SIOAs in solving engineering problems, such as design optimization, production scheduling, network routing, and control systems.

By publishing high-quality research on SIOAs and their applications, this Special Issue aims to promote the dissemination of knowledge, facilitate interdisciplinary collaborations, and inspire further advancements in this exciting field. We encourage researchers to submit their original work, addressing both theoretical and applied aspects of SIOAs, to contribute to this important endeavor.

Prof. Dr. Heming Jia
Prof. Dr. Gang Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • swarm intelligence optimization algorithms
  • particle swarm optimization algorithm
  • optimization algorithms
  • meta-heuristics
  • swarm intelligence
  • engineering applications
  • engineering design problems
  • real-world applications
  • constraint handling
  • benchmarks
  • novel approaches
  • complicated optimization problems

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Published Papers (6 papers)

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Research

32 pages, 4886 KiB  
Article
Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference
by Weiping Meng, Yang He and Yongquan Zhou
Biomimetics 2025, 10(1), 57; https://doi.org/10.3390/biomimetics10010057 - 15 Jan 2025
Viewed by 574
Abstract
This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from [...] Read more.
This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from falling into a local optimum, the Gaussian mutation mechanism with dynamic variance was introduced, and the migration mutation mechanism was also used to enhance the population diversity of the algorithm. Eighteen benchmark functions were used to compare the proposed method with five classical metaheuristic algorithms and three BOA variable optimization methods. The QLBOA was used to solve the green vehicle routing problem with time windows considering customer preferences. The influence of decision makers’ subjective preferences and weight factors on fuel consumption, carbon emissions, penalty cost, and total cost are analyzed. Compared with three classical optimization algorithms, the experimental results show that the proposed QLBOA has a generally superior performance. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)
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20 pages, 7258 KiB  
Article
MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification
by Guangyu Mu, Jiaxue Li, Zhanhui Liu, Jiaxiu Dai, Jiayi Qu and Xiurong Li
Biomimetics 2025, 10(1), 41; https://doi.org/10.3390/biomimetics10010041 - 10 Jan 2025
Viewed by 570
Abstract
With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation [...] Read more.
With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method’s principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder–Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)
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25 pages, 4843 KiB  
Article
Ameliorated Chameleon Algorithm-Based Shape Optimization of Disk Wang–Ball Curves
by Yan Liang, Rui Yang, Xianzhi Hu and Gang Hu
Biomimetics 2025, 10(1), 3; https://doi.org/10.3390/biomimetics10010003 - 24 Dec 2024
Viewed by 487
Abstract
The shape design and optimization of complex disk curves is a crucial and intractable technique in computer-aided design and manufacturing (CAD/CAM). Based on disk Wang–Ball (DWB) curves, this paper defines a novel combined disk Wang–Ball (CDWB) curve with constrained parameters and investigates the [...] Read more.
The shape design and optimization of complex disk curves is a crucial and intractable technique in computer-aided design and manufacturing (CAD/CAM). Based on disk Wang–Ball (DWB) curves, this paper defines a novel combined disk Wang–Ball (CDWB) curve with constrained parameters and investigates the shape optimization of CDWB curves by using the multi-strategy ameliorated chameleon swarm algorithm (MCSA). Firstly, in order to meet the various shape design requirements, the CDWB curves consisting of n DWB curves are defined, and the G1 and G2 geometric continuity conditions for the curves are derived. Secondly, the shape optimization of CDWB curves is considered as a minimization problem with curve energy as the objective, and an optimization model is developed under the constraints of the splicing conditions. Finally, the meta-heuristic algorithm MCSA is introduced to solve the established optimization model to obtain the minimum energy value, and its performance is verified by comparison with other algorithms. The results of representative numerical examples confirm the effectiveness and competitiveness of the MCSA for the CDWB curve shape optimization problems. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)
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13 pages, 1279 KiB  
Article
Predictive Modeling of Hospital Readmission of Schizophrenic Patients in a Spanish Region Combining Particle Swarm Optimization and Machine Learning Algorithms
by Susel Góngora Alonso, Isabel Herrera Montano, Isabel De la Torre Díez, Manuel Franco-Martín, Mohammed Amoon, Jesús-Angel Román-Gallego and María-Luisa Pérez-Delgado
Biomimetics 2024, 9(12), 752; https://doi.org/10.3390/biomimetics9120752 - 11 Dec 2024
Viewed by 701
Abstract
Readmissions are an indicator of hospital care quality; a high readmission rate is associated with adverse outcomes. This leads to an increase in healthcare costs and quality of life for patients. Developing predictive models for hospital readmissions provides opportunities to select treatments and [...] Read more.
Readmissions are an indicator of hospital care quality; a high readmission rate is associated with adverse outcomes. This leads to an increase in healthcare costs and quality of life for patients. Developing predictive models for hospital readmissions provides opportunities to select treatments and implement preventive measures. The aim of this study is to develop predictive models for the readmission risk of patients with schizophrenia, combining the particle swarm optimization (PSO) algorithm with machine learning classification algorithms. The database used in the study includes a total of 6089 readmission records of patients with schizophrenia. These records were collected from 11 public hospitals in Castilla and León, Spain, in the period 2005–2015. The results of the study show that the Random Forest algorithm combined with PSO achieved the best results across the evaluated performance metrics: AUC = 0.860, recall = 0.959, accuracy = 0.844, and F1-score = 0.907. The development of these new models contributes to -improving patient care. Additionally, they enable preventive measures to reduce costs in healthcare systems. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)
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42 pages, 13108 KiB  
Article
AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems
by Guoping You, Zengtong Lu, Zhipeng Qiu and Hao Cheng
Biomimetics 2024, 9(12), 727; https://doi.org/10.3390/biomimetics9120727 - 28 Nov 2024
Viewed by 852
Abstract
Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented [...] Read more.
Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO). The adaptive population learning strategy is proposed to improve the global exploration capability of BWO. The introduction of the roulette equilibrium selection strategy allows BWO to have more reference points to choose among during the exploitation phase, which enhances the flexibility of the algorithm. In addition, the adaptive avoidance strategy improves the algorithm’s ability to escape from local optima and enriches the population quality. In order to validate the performance of the proposed AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on the CEC2017 and CEC2022 test sets. Statistical tests, convergence analysis, and stability analysis show that the AMBWO exhibits a superior overall performance. Finally, the applicability and superiority of the AMBWO was further verified by several engineering optimization problems. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)
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20 pages, 13202 KiB  
Article
A Multi-Robot Task Allocation Method Based on the Synergy of the K-Means++ Algorithm and the Particle Swarm Algorithm
by Youdong Yuan, Ping Yang, Hanbing Jiang and Tiange Shi
Biomimetics 2024, 9(11), 694; https://doi.org/10.3390/biomimetics9110694 - 13 Nov 2024
Viewed by 1024
Abstract
Addressing challenges in the traditional K-means algorithm, such as the challenge of selecting initial clustering center points and the lack of a maximum limit on the number of clusters, and where the set of tasks in the clusters is not reasonably sorted after [...] Read more.
Addressing challenges in the traditional K-means algorithm, such as the challenge of selecting initial clustering center points and the lack of a maximum limit on the number of clusters, and where the set of tasks in the clusters is not reasonably sorted after the task assignment, which makes the cooperative operation of multiple robots inefficient, this paper puts forward a multi-robot task assignment method based on the synergy of the K-means++ algorithm and the particle swarm optimization (PSO) algorithm. According to the processing capability of the robots, the K-means++ algorithm that limits the maximum number of clusters is used to cluster the target points of the task. The clustering results are assigned to the multi-robot system using the PSO algorithm based on the distances between the robots and the centers of the clusters, which divides the multi-robot task assignment problem into a multiple traveling salesmen problem. Then, the PSO algorithm is used to optimize the ordering of the task sets in each cluster for the multiple traveling salesmen problem. An experimental verification platform is established by building a simulation and physical experiment platform utilizing the Robot Operating System (ROS). The findings indicate that the proposed algorithm outperforms both the clustering-based market auction algorithm and the non-clustering particle swarm algorithm, enhancing the efficiency of collaborative operations among multiple robots. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)
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