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Biomimetics
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Nature-Inspired Computer Algorithms: 2nd Edition
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Nature-Inspired Computer Algorithms: 2nd Edition

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

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 20973

Special Issue Editors

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
Dr. Weiguo Zhao

E-Mail Website
Guest Editor
School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, China
Interests: computing intelligence; artificial intelligence; intelligent water conservancy; intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenxing Zhang

E-Mail Website
Guest Editor
Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
Interests: multi-objective optimizations; stochastic and deterministic hydrologic modelling; water resources systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, hard optimization problems that cannot be solved within an acceptable computational time by deterministic mathematical optimization methods have been successfully solved by nature-inspired computer algorithms.

These kinds of algorithms are usually inspired by real-world phenomena and solve optimization problems by simulating physical rules or biological phenomena. For example, one of the most classical nature-inspired algorithms, genetic algorithms, incorporates natural operators of selection, crossover, and mutation in its search process.

Due to their simple structure and low requirements for target problems, nature-inspired computer algorithms are widely used to solve engineering, prediction, and optimal control problems, as well as clustering, classification, and deep learning problems. They are specifically designed to search for, generate, or select heuristic results that provide a sufficient solution to an optimization problem, especially with incomplete information or limited computational power.

Thhis Special Issue aims to encourage researchers to discuss topics surrounding the theories and applications based on nature-inspired computer algorithms. Topics of interest for this symposium include, but are not limited to:

  • The construction and theoretical analysis of some novel or unique nature-inspired computer algorithms;
  • Applications of nature-inspired computer algorithms for real-world problems;
  • Strategies or ideas to enhance the nature-inspired computer algorithms’ capacities by combining the structural characteristics of real-world problems and biological phenomena of algorithms.

Prof. Dr. Gang Hu
Dr. Weiguo Zhao
Dr. Zhenxing Zhang
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

  • nature-inspired computer algorithms
  • bio-inspired algorithms
  • evolutionary algorithms
  • swarm-intelligent optimization algorithms
  • bio-inspired intelligent algorithms
  • large-scale global optimization
  • mathematical analysis of nature-inspired algorithms
  • multi-objective optimization algorithms
  • nature-inspired computer algorithms applications to: path planning, complex networks, feature selection, classification, forecasting, image processing, deep learning, geometric shape optimization, mechanical optimization design, engineering design, biomedical, energy systems, water resources systems, and others

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (12 papers)

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Research

37 pages, 4291 KiB  
Article
A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems
by Abdelazim G. Hussien, Adrian Pop, Sumit Kumar, Fatma A. Hashim and Gang Hu
Biomimetics 2024, 9(3), 186; https://doi.org/10.3390/biomimetics9030186 - 19 Mar 2024
Viewed by 1799
Abstract
The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb’s law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome [...] Read more.
The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb’s law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Lévy flights, simulated annealing, and the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC’17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC’17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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18 pages, 2852 KiB  
Article
Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms
by Jaehyeong Lee, Yourim Yoon, Jiyoun Kim and Yong-Hyuk Kim
Biomimetics 2024, 9(3), 179; https://doi.org/10.3390/biomimetics9030179 - 15 Mar 2024
Viewed by 1831
Abstract
This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th [...] Read more.
This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection’s pivotal role in future sarcopenia research. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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24 pages, 2530 KiB  
Article
A Two-Layer Self-Organizing Map with Vector Symbolic Architecture for Spatiotemporal Sequence Learning and Prediction
by Thimal Kempitiya, Damminda Alahakoon, Evgeny Osipov, Sachin Kahawala and Daswin De Silva
Biomimetics 2024, 9(3), 175; https://doi.org/10.3390/biomimetics9030175 - 13 Mar 2024
Cited by 1 | Viewed by 1584
Abstract
We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using [...] Read more.
We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using the learned spatial patterns as an alphabet of elements. The decoupling, motivated by cognitive neuroscience research, provides the flexibility for fast and adaptive learning with dynamic changes to data and concept drift and as such is better suited for real-time learning and prediction. The algorithm further addresses several key computational requirements for predicting the next occurrences based on real-life spatiotemporal data, which have been found to be challenging with current state-of-the-art algorithms. Firstly, spatial and temporal patterns are detected using unsupervised learning from unlabeled data streams in changing environments; secondly, vector symbolic architecture (VSA) is used to manage variable-length sequences; and thirdly, hyper dimensional (HD) computing-based associative memory is used to facilitate the continuous prediction of the next occurrences in sequential patterns. The algorithm has been empirically evaluated using two benchmark and three time-series datasets to demonstrate its advantages compared to the state-of-the-art in spatiotemporal unsupervised sequence learning where the proposed ST-SOM algorithm is able to achieve 45% error reduction compared to HTM algorithm. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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21 pages, 3603 KiB  
Article
Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves
by Jing Lu, Rui Yang, Gang Hu and Abdelazim G. Hussien
Biomimetics 2024, 9(3), 134; https://doi.org/10.3390/biomimetics9030134 - 22 Feb 2024
Viewed by 1933
Abstract
A method for the approximate merging of disk Wang–Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional [...] Read more.
A method for the approximate merging of disk Wang–Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional curves to disk curves and viewing it as an optimization problem, an approximate merging model is established to minimize the merging error through an error formulation. Considering the complexity of the model built, a BEESO with better convergence accuracy and convergence speed is introduced, which combines the snake optimizer (SO) and three strategies including bi-directional search, evolutionary population dynamics, and elite opposition-based learning. The merging results and merging errors of numerical examples demonstrate that BEESO is effective in solving approximate merging models, and it provides a new method for the compression and transfer of product shape data in Computer-Aided Geometric Design. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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44 pages, 3468 KiB  
Article
Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving
by Marcelo Becerra-Rozas, Broderick Crawford, Ricardo Soto, El-Ghazali Talbi and Jose M. Gómez-Pulido
Biomimetics 2024, 9(2), 89; https://doi.org/10.3390/biomimetics9020089 - 1 Feb 2024
Viewed by 1268
Abstract
In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving [...] Read more.
In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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32 pages, 6118 KiB  
Article
Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering
by Megha Varshney, Pravesh Kumar, Musrrat Ali and Yonis Gulzar
Biomimetics 2024, 9(1), 54; https://doi.org/10.3390/biomimetics9010054 - 18 Jan 2024
Cited by 3 | Viewed by 1324
Abstract
The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited [...] Read more.
The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific situations. To increase the exploration ability of the AO algorithm, this work offers a hybrid approach that employs the alpha position of the Grey Wolf Optimizer (GWO) to drive the search process of the AO algorithm. At the same time, we applied the quasi-opposition-based learning (QOBL) strategy in each phase of the Aquila Optimizer algorithm. This strategy develops quasi-oppositional solutions to current solutions. The quasi-oppositional solutions are then utilized to direct the search phase of the AO algorithm. The GWO method is also notable for its resistance to noise. This means that it can perform effectively even when the objective function is noisy. The AO algorithm, on the other hand, may be sensitive to noise. By integrating the GWO approach into the AO algorithm, we can strengthen its robustness to noise, and hence, improve its performance in real-world issues. In order to evaluate the effectiveness of the technique, the algorithm was benchmarked on 23 well-known test functions and CEC2017 test functions and compared with other popular metaheuristic algorithms. The findings demonstrate that our proposed method has excellent efficacy. Finally, it was applied to five practical engineering issues, and the results showed that the technique is suitable for tough problems with uncertain search spaces. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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30 pages, 4803 KiB  
Article
Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features
by Xiyuan Liu, Liying Wang, Hongyan Yan, Qingjiao Cao, Luyao Zhang and Weiguo Zhao
Biomimetics 2024, 9(1), 32; https://doi.org/10.3390/biomimetics9010032 - 4 Jan 2024
Cited by 1 | Viewed by 1420
Abstract
To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform [...] Read more.
To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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26 pages, 2272 KiB  
Article
An Improved Harris Hawks Optimization Algorithm and Its Application in Grid Map Path Planning
by Lin Huang, Qiang Fu and Nan Tong
Biomimetics 2023, 8(5), 428; https://doi.org/10.3390/biomimetics8050428 - 15 Sep 2023
Cited by 4 | Viewed by 1892
Abstract
Aimed at the problems of the Harris Hawks Optimization (HHO) algorithm, including the non-origin symmetric interval update position out-of-bounds rate, low search efficiency, slow convergence speed, and low precision, an Improved Harris Hawks Optimization (IHHO) algorithm is proposed. In this algorithm, a circle [...] Read more.
Aimed at the problems of the Harris Hawks Optimization (HHO) algorithm, including the non-origin symmetric interval update position out-of-bounds rate, low search efficiency, slow convergence speed, and low precision, an Improved Harris Hawks Optimization (IHHO) algorithm is proposed. In this algorithm, a circle map was added to replace the pseudo-random initial population, and the population boundary number was reduced to improve the efficiency of the location update. By introducing a random-oriented strategy, the information exchange between populations was increased and the out-of-bounds position update was reduced. At the same time, the improved sine-trend search strategy was introduced to improve the search performance and reduce the out-of-bound rate. Then, a nonlinear jump strength combining escape energy and jump strength was proposed to improve the convergence accuracy of the algorithm. Finally, the simulation experiment was carried out on the test function and the path planning application of a 2D grid map. The results show that the Improved Harris Hawks Optimization algorithm is more competitive in solving accuracy, convergence speed, and non-origin symmetric interval search efficiency, and verifies the feasibility and effectiveness of the Improved Harris Hawks Optimization in the path planning of a grid map. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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23 pages, 1110 KiB  
Article
Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking
by Ebubekir Kaya, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever, Başak Öztürk and Bilgin Yazlık
Biomimetics 2023, 8(5), 402; https://doi.org/10.3390/biomimetics8050402 - 1 Sep 2023
Cited by 3 | Viewed by 1739
Abstract
One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in [...] Read more.
One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10−4, 1.6 × 10−3, and 2.3 × 10−3, respectively. The test error values are 4.6 × 10−4, 1.6 × 10−3, and 2.4 × 10−3, respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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21 pages, 2128 KiB  
Article
Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems
by Hongwei Ding, Yuting Liu, Zongshan Wang, Gushen Jin, Peng Hu and Gaurav Dhiman
Biomimetics 2023, 8(5), 383; https://doi.org/10.3390/biomimetics8050383 - 23 Aug 2023
Cited by 3 | Viewed by 1472
Abstract
The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these [...] Read more.
The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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40 pages, 3234 KiB  
Article
AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems
by Yanpu Zhao, Changsheng Huang, Mengjie Zhang and Yang Cui
Biomimetics 2023, 8(4), 381; https://doi.org/10.3390/biomimetics8040381 - 21 Aug 2023
Cited by 3 | Viewed by 1448
Abstract
The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes [...] Read more.
The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes a metaheuristic algorithm for continuous and constrained global optimization problems, which combines the MOA, the Aquila Optimizer (AO), and the opposition-based learning (OBL) strategy, called AOBLMOA, to overcome the shortcomings of the MOA. The proposed algorithm first fuses the high soar with vertical stoop method and the low flight with slow descent attack method in the AO into the position movement process of the male mayfly population in the MOA. Then, it incorporates the contour flight with short glide attack and the walk and grab prey methods in the AO into the positional movement of female mayfly populations in the MOA. Finally, it replaces the gene mutation behavior of offspring mayfly populations in the MOA with the OBL strategy. To verify the optimization ability of the new algorithm, we conduct three sets of experiments. In the first experiment, we apply AOBLMOA to 19 benchmark functions to test whether it is the optimal strategy among multiple combined strategies. In the second experiment, we test AOBLMOA by using 30 CEC2017 numerical optimization problems and compare it with state-of-the-art metaheuristic algorithms. In the third experiment, 10 CEC2020 real-world constrained optimization problems are used to demonstrate the applicability of AOBLMOA to engineering design problems. The experimental results show that the proposed AOBLMOA is effective and superior and is feasible in numerical optimization problems and engineering design problems. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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18 pages, 1842 KiB  
Article
The Application of the Improved Jellyfish Search Algorithm in a Site Selection Model of an Emergency Logistics Distribution Center Considering Time Satisfaction
by Ping Li and Xingqi Fan
Biomimetics 2023, 8(4), 349; https://doi.org/10.3390/biomimetics8040349 - 6 Aug 2023
Cited by 2 | Viewed by 1965
Abstract
In an emergency situation, fast and efficient logistics and distribution are essential for minimizing the impact of a disaster and for safeguarding property. When selecting a distribution center location, time satisfaction needs to be considered, in addition to the general cost factor. The [...] Read more.
In an emergency situation, fast and efficient logistics and distribution are essential for minimizing the impact of a disaster and for safeguarding property. When selecting a distribution center location, time satisfaction needs to be considered, in addition to the general cost factor. The improved jellyfish search algorithm (CIJS), which simulates the bionics of jellyfish foraging, is applied to solve the problem of an emergency logistics and distribution center site selection model considering time satisfaction. The innovation of the CIJS is mainly reflected in two aspects. First, when initializing the population, the two-level logistic map method is used instead of the original logistic map method to improve the diversity and uniform distribution of the population. Second, in the jellyfish search process, a Cauchy strategy is introduced to determine the moving distance of internal motions, which improves the global search capability and prevents the search from falling into local optimal solutions. The superiority of the improved algorithm was verified by testing 20 benchmark functions and applying them to site selection problems of different dimensions. The performance of the CIJS was compared to that of heuristic algorithms through the iterative convergence graph of the algorithm. The experimental results show that the CIJS has higher solution accuracy and faster solution speed than PSO, the WOA, and JS. Full article
(This article belongs to the Special Issue Nature-Inspired Computer Algorithms: 2nd Edition)
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