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Mathematics
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Nature Inspired Computing and Optimisation
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Nature Inspired Computing and Optimisation

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 13649

Special Issue Editors

Prof. Dr. Srikanta Patnaik

E-Mail Website
Guest Editor
Department of Computer Science and Engineering & Director (International Relation and Publication), SOA University, Bhubaneswar 751030, Odisha, India
Interests: artificial intelligence; computational intelligence; soft computing applications; re-engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kazumi Nakamatsu

E-Mail Website
Guest Editor
School of Human Science and Environment, University of Hyogo Shinzaike-hon-cho, Himeji 670-0092, Japan
Interests: logic and their applications to computer science; paraconsistent annotated logic programs and their applications

Special Issue Information

Dear Colleagues,

Nature-inspired computing is an interdisciplinary field and a branch of artificial intelligence that employs processes, which is involved in evolution of living beings, biology and nature for solving complex problems. Artificial Intelligence covers higher level processes like problem solving, reasoning, making inferences in abstract and virtual worlds that consist of precisely defined states and operations in closed systems unlike real world. During the last decade, due to the convergence of bio, nano and information technology, an appreciable amount of research has been done on sensors and actuators, which leads to the design of many autonomous agents and robots and subsequently the development of Multi Agent Systems (MAS) for real world applications. Usually real-time systems are complex, inconsistent, uncertain and imprecise, which is difficult to handle using traditional methods. But inspiring nature has leaded to finding clues for solution of unsolved problems of this kind. Nature-inspired computing has been existing since last many decades in the form of bio-inspired computing which includes small number of tools like evolutionary computing, neural networks, particle swarm optimization, and ant colony optimization. Gradually other forms of intelligence are added to nature-inspired computing such as artificial bee colony optimization, cuckoo search, firefly algorithm, bacteria foraging optimization algorithm, intelligent water drop optimization, gravitational search, and harmony search along with many more. These computational techniques use biological or evolutionary processes as main component in the efficient design and implementation of the artificial intelligent algorithms.Nature-inspired computing can be applied to a variety of problems, ranging from engineering and scientific research to manufacturing industry and business as they provide robust and flexible solutions along with learning and adaptability as compared to traditional optimization techniques. It can be applied to solve optimization issues in any domain and may be unified as Nature-inspired computing and optimization (NICO). While studying the nature, researchers found a number of cases from ant, bee, flock of birds, frog, cuckoo, firefly, bat, crocodile etc., which can be modelled for real-world problem. Looking at the emergence of bio, info and nano technology we are confident that the field of the nature-inspired computing and optimization can solve many complex real world problems

Prof. Dr. Srikanta Patnaik
Prof. Dr. Kazumi Nakamatsu
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • ant colony optimization (ACO)
  • bee colony optimization
  • honey-bee mating dance
  • bacterial foraging optimization
  • particle swarm optimization
  • shuffled leaping-frog algorithm,
  • artificial immune systems
  • memetic algorithms (MA)
  • simulated annealing (SA)
  • intelligent water drops
  • gravitational search
  • river formation dynamics
  • charged system search
  • stochastic diffusion search
  • threshold acceptance (TA)
  • tabu search (TS)
  • variable neighborhood search (VNS)
  • iterative local search (ILS)
  • guided local search (GLS)
  • genetic algorithms (GA)
  • scatter search (SS)
  • greedy randomized adaptive search procedure (GRASP)
  • the harmony method
  • cuckoo search
  • firefly algorithm
  • bat algorithm
  • flower pollination algorithm
  • ruin and recreate
  • the great deluge algorithm
  • human brain function inspired algorithms

Published Papers (6 papers)

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Research

14 pages, 2351 KiB  
Article
A Novel Concept-Cognitive Learning Method for Bird Song Classification
by Jing Lin, Wenkan Wen and Jiyong Liao
Mathematics 2023, 11(20), 4298; https://doi.org/10.3390/math11204298 - 16 Oct 2023
Cited by 1 | Viewed by 720
Abstract
Bird voice classification is a crucial issue in wild bird protection work. However, the existing strategies of static classification are always unable to achieve the desired outcomes in a dynamic data stream context, as the standard machine learning approaches mainly focus on static [...] Read more.
Bird voice classification is a crucial issue in wild bird protection work. However, the existing strategies of static classification are always unable to achieve the desired outcomes in a dynamic data stream context, as the standard machine learning approaches mainly focus on static learning, which is not suitable for mining dynamic data and has the disadvantages of high computational overhead and hardware requirements. Therefore, these shortcomings greatly limit the application of standard machine learning approaches. This study aims to quickly and accurately distinguish bird species by their sounds in bird conservation work. For this reason, a novel concept-cognitive computing system (C3S) framework, namely, PyC3S, is proposed for bird sound classification in this paper. The proposed system uses feature fusion and concept-cognitive computing technology to construct a Python version of a dynamic bird song classification and recognition model on a dataset containing 50 species of birds. The experimental results show that the model achieves 92.77% accuracy, 92.26% precision, 92.25% recall, and a 92.41% F1-Score on the given 50 bird datasets, validating the effectiveness of our PyC3S compared to the state-of-the-art stream learning algorithms. Full article
(This article belongs to the Special Issue Nature Inspired Computing and Optimisation)
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19 pages, 4689 KiB  
Article
Performance Augmentation of Cuckoo Search Optimization Technique Using Vector Quantization in Image Compression
by Aditya Bakshi, Akhil Gupta, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro, Fayez Alqahtani, Amr Tolba and Maria Simona Raboaca
Mathematics 2023, 11(10), 2364; https://doi.org/10.3390/math11102364 - 19 May 2023
Viewed by 941
Abstract
For constructing the best local codebook for image compression, there are many Vector Quantization (VQ) procedures, but the simplest VQ procedure is the Linde–Buzo–Gray (LBG) procedure. Techniques such as the Gaussian Dissemination Function (GDF) are used for the searching process in generating a [...] Read more.
For constructing the best local codebook for image compression, there are many Vector Quantization (VQ) procedures, but the simplest VQ procedure is the Linde–Buzo–Gray (LBG) procedure. Techniques such as the Gaussian Dissemination Function (GDF) are used for the searching process in generating a global codebook for particle swarm optimization (PSO), Honeybee mating optimization (HBMO), and Firefly (FA) procedures. However, when particle velocity is very high, FA encounters a problem when brighter fireflies are trivial, and PSO suffers uncertainty in merging. A novel procedure, Cuckoo Search–Kekre Fast Codebook Generation (CS-KFCG), is proposed that enhances Cuckoo Search–Linde–Buzo–Gray (CS-LBG) codebook by implementing a Flight Dissemination Function (FDF), which produces more speed than other states of the art algorithms with appropriate mutation expectations for the overall codebook. Also, CS-KFGC has generated a high Peak Signal Noise Ratio (PSNR) in terms of high duration (time) and better acceptability rate. Full article
(This article belongs to the Special Issue Nature Inspired Computing and Optimisation)
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16 pages, 9902 KiB  
Article
A Novel Reconstruction of the Sparse-View CBCT Algorithm for Correcting Artifacts and Reducing Noise
by Jie Zhang, Bing He, Zhengwei Yang and Weijie Kang
Mathematics 2023, 11(9), 2127; https://doi.org/10.3390/math11092127 - 01 May 2023
Cited by 1 | Viewed by 1496
Abstract
X-ray tomography is often affected by noise and artifacts during the reconstruction process, such as detector offset, calibration errors, metal artifacts, etc. Conventional algorithms, including FDK and SART, are unable to satisfy the sampling theorem requirements for 3D reconstruction under sparse-view constraints, exacerbating [...] Read more.
X-ray tomography is often affected by noise and artifacts during the reconstruction process, such as detector offset, calibration errors, metal artifacts, etc. Conventional algorithms, including FDK and SART, are unable to satisfy the sampling theorem requirements for 3D reconstruction under sparse-view constraints, exacerbating the impact of noise and artifacts. This paper proposes a novel 3D reconstruction algorithm tailored to sparse-view cone-beam computed tomography (CBCT). Drawing upon compressed sensing theory, we incorporate the weighted Schatten p-norm minimization (WSNM) algorithm for 2D image denoising and the adaptive steepest descent projection onto convex sets (ASD-POCS) algorithm, which employs a total variation (TV) regularization term. These inclusions serve to reduce noise and ameliorate artifacts. Our proposed algorithm extends the WSNM approach into three-dimensional space and integrates the ASD-POCS algorithm, enabling 3D reconstruction with digital brain phantoms, clinical medical data, and real projections from our portable CBCT system. The performance of our algorithm surpasses traditional methods when evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. Furthermore, our approach demonstrates marked enhancements in artifact reduction and noise suppression. Full article
(This article belongs to the Special Issue Nature Inspired Computing and Optimisation)
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17 pages, 2779 KiB  
Article
Improved Beluga Whale Optimization for Solving the Simulation Optimization Problems with Stochastic Constraints
by Shih-Cheng Horng and Shieh-Shing Lin
Mathematics 2023, 11(8), 1854; https://doi.org/10.3390/math11081854 - 13 Apr 2023
Cited by 8 | Viewed by 1478
Abstract
Simulation optimization problems with stochastic constraints are optimization problems with deterministic cost functions subject to stochastic constraints. Solving the considered problem by traditional optimization approaches is time-consuming if the search space is large. In this work, an approach integration of beluga whale optimization [...] Read more.
Simulation optimization problems with stochastic constraints are optimization problems with deterministic cost functions subject to stochastic constraints. Solving the considered problem by traditional optimization approaches is time-consuming if the search space is large. In this work, an approach integration of beluga whale optimization and ordinal optimization is presented to resolve the considered problem in a relatively short time frame. The proposed approach is composed of three levels: emulator, diversification, and intensification. Firstly, the polynomial chaos expansion is treated as an emulator to evaluate a design. Secondly, the improved beluga whale optimization is proposed to seek N candidates from the whole search space. Eventually, the advanced optimal computational effort allocation is adopted to determine a superior design from the N candidates. The proposed approach is utilized to seek the optimal number of service providers for minimizing staffing costs while delivering a specific level of care in emergency department healthcare. A practical example of an emergency department with six cases is used to verify the proposed approach. The CPU time consumes less than one minute for six cases, which demonstrates that the proposed approach can meet the requirement of real-time application. In addition, the proposed approach is compared to five heuristic methods. Empirical tests indicate the efficiency and robustness of the proposed approach. Full article
(This article belongs to the Special Issue Nature Inspired Computing and Optimisation)
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12 pages, 2888 KiB  
Article
Multimodal Movie Recommendation System Using Deep Learning
by Yongheng Mu and Yun Wu
Mathematics 2023, 11(4), 895; https://doi.org/10.3390/math11040895 - 10 Feb 2023
Cited by 14 | Viewed by 6798
Abstract
Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over [...] Read more.
Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis. Full article
(This article belongs to the Special Issue Nature Inspired Computing and Optimisation)
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32 pages, 3949 KiB  
Article
EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications
by Gang Hu, Jiao Wang, Min Li, Abdelazim G. Hussien and Muhammad Abbas
Mathematics 2023, 11(4), 851; https://doi.org/10.3390/math11040851 - 07 Feb 2023
Cited by 28 | Viewed by 1509
Abstract
The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS [...] Read more.
The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS algorithm still has significant development space for solving complex optimization problems with high dimensions and multiple local optima. Therefore, in this study, an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) By adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed. (ii) By adding a local escape operator, the algorithm can skip the trap of local optimization, and thereby, can enhance the exploitation ability of the JS algorithm. (iii) By applying an opposition-based learning and quasi-opposition learning strategy, the population distribution is increased, strengthened, and more diversified, and better individuals are selected from the present and the new opposition solution to participate in the next iteration, which can enhance the solution’s quality, meanwhile, convergence speed is faster and the algorithm’s precision is increased. In addition, the performance of the developed EJS algorithm was compared with those of the incomplete improved algorithms, and some previously outstanding and advanced methods were evaluated on the CEC2019 test set as well as six examples of real engineering cases. The results demonstrate that the EJS algorithm can skip the trap of local optimization, can enhance the solution’s quality, and can increase the calculation speed. In addition, the practical engineering applications of the EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and therefore, suggests future possible applications for solving such optimization problems. Full article
(This article belongs to the Special Issue Nature Inspired Computing and Optimisation)
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