Nature-Inspired Metaheuristic Optimization Algorithms 2024

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 8789

Special Issue Editors


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Department of Land Surveying and Geo-Informatics, Smart City Research Institute, The Hong Kong Polytechnic University, Hong Kong, China
Interests: SLAM; control systems; robotics; machine learning
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Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Interests: robotic manipulation; autonomous manufacturing; multi-robot coordination; intelligent control and optimization
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College of Electrical & Mechanical Engineering, National University of Sciences & Technology (NUST), Islamabad, Pakistan
Interests: micro and nano robotics; AFM imaging; mobile robotics; nano materials
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Special Issue Information

Dear Colleagues,

Developing computationally efficient algorithms has been at the forefront of research and development in recent years. With the advent of big data, deep learning, and artificial intelligence (AI), prioritizing computationally efficient software and hardware systems has become a primary design objective. Optimization algorithms are an integral part of all real-world systems. Although traditional gradient-based optimization methods have been rigorously studied over the years, they put several analytical constraints on the objective function, e.g., continuity, differentiability, and convexity. Additionally, an analytical model of the system should be a priori, which can be difficult to formulate for several real-world systems. These algorithms also do not apply to discontinuous and discrete systems. Even if the analytical model is known to be continuous and differentiable, the computational requirement of gradient and Hessians makes them expensive to implement.

Metaheuristic optimization algorithms inspired by natural processes and the behavior of biological organisms present themselves as an effective alternative to the traditional gradient-based algorithms. They have also been extensively explored in recent years and are rapidly finding applications in real-world systems. These algorithms are formulated on the principles of biomimetics, i.e., mimicking the behavior of biological systems to solve an optimization problem. The behavior of biological organisms has been optimized over millions of years through the process of natural selection. Every species has developed traits (mostly instinctual) necessary for survival in nature. Modeling this behavior as a mathematical algorithm presents great potential to develop computationally efficient optimization algorithms. For example, evolutionary algorithms (EAs) and genetic algorithms (GAs) are inspired by the process of genetic mutations and the survival of the fittest. Similarly, other algorithms like the particle swarm optimizer (PSO), grey wolf optimizer (GWO), and beetle antennae search (BAS) are inspired by the behavior of birds and insects and their ability to accomplish a task in a decentralized manner by just following their basic biological instincts and not needing any elaborate planning and centralized communication.

We are organizing this Special Issue to gather the latest research related to nature-inspired metaheuristic optimization algorithms and their applications. The application of a bio-inspired metaheuristic algorithm in real-world systems will draw greater research attention to biomimetics.

Dr. Ameer Hamza Khan
Prof. Dr. Shuai Li
Dr. Danish Hussain
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

  • bio-inspired algorithms
  • metaheuristic optimization

Published Papers (10 papers)

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Research

28 pages, 1514 KiB  
Article
Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices
by Rodrigo Olivares, Rene Noel, Sebastián M. Guzmán, Diego Miranda and Roberto Munoz
Biomimetics 2024, 9(5), 292; https://doi.org/10.3390/biomimetics9050292 - 13 May 2024
Viewed by 465
Abstract
One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as [...] Read more.
One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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30 pages, 3568 KiB  
Article
Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications
by Mingjun Ye , Heng Zhou, Haoyu Yang, Bin Hu and Xiong Wang
Biomimetics 2024, 9(5), 291; https://doi.org/10.3390/biomimetics9050291 - 13 May 2024
Viewed by 435
Abstract
The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. [...] Read more.
The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed “Mean Differential Variation”, to enhance the algorithm’s ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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33 pages, 3236 KiB  
Article
A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm
by Dante Leiva, Benjamín Ramos-Tapia, Broderick Crawford, Ricardo Soto and Felipe Cisternas-Caneo
Biomimetics 2024, 9(5), 283; https://doi.org/10.3390/biomimetics9050283 - 9 May 2024
Viewed by 394
Abstract
The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for [...] Read more.
The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional integer programming solutions may become inefficient in real-life instances. Given this complexity, various metaheuristics have been successfully applied to solve the set-covering problem and related issues. This study introduces, implements, and analyzes a novel metaheuristic inspired by the well-established Growth Optimizer algorithm. Drawing insights from human behavioral patterns, this approach has shown promise in optimizing complex problems in continuous domains, where experimental results demonstrate the effectiveness and competitiveness of the metaheuristic compared to other strategies. The Growth Optimizer algorithm is modified and adapted to the realm of binary optimization for solving the set-covering problem, resulting in the creation of the Binary Growth Optimizer algorithm. Upon the implementation and analysis of its outcomes, the findings illustrate its capability to achieve competitive and efficient solutions in terms of resolution time and result quality. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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27 pages, 9294 KiB  
Article
A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application
by Shijie Jiang, Yinggao Yue, Changzu Chen, Yaodan Chen and Li Cao
Biomimetics 2024, 9(5), 270; https://doi.org/10.3390/biomimetics9050270 - 28 Apr 2024
Viewed by 573
Abstract
The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, [...] Read more.
The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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25 pages, 3551 KiB  
Article
A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging
by Amir Hossein Sheikh Azadi, Mohammad Khalilzadeh, Jurgita Antucheviciene, Ali Heidari and Amirhossein Soon
Biomimetics 2024, 9(4), 242; https://doi.org/10.3390/biomimetics9040242 - 18 Apr 2024
Viewed by 1104
Abstract
Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied [...] Read more.
Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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25 pages, 9002 KiB  
Article
Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems
by Megha Varshney, Pravesh Kumar, Musrrat Ali and Yonis Gulzar
Biomimetics 2024, 9(4), 215; https://doi.org/10.3390/biomimetics9040215 - 4 Apr 2024
Viewed by 694
Abstract
One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of [...] Read more.
One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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30 pages, 4929 KiB  
Article
A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems
by Yujia Liu, Yuan Zeng, Rui Li, Xingyun Zhu, Yuemai Zhang, Weijie Li, Taiyong Li, Donglin Zhu and Gangqiang Hu
Biomimetics 2024, 9(4), 204; https://doi.org/10.3390/biomimetics9040204 - 28 Mar 2024
Viewed by 820
Abstract
In today’s fast-paced and ever-changing environment, the need for algorithms with enhanced global optimization capability has become increasingly crucial due to the emergence of a wide range of optimization problems. To tackle this issue, we present a new algorithm called Random Particle Swarm [...] Read more.
In today’s fast-paced and ever-changing environment, the need for algorithms with enhanced global optimization capability has become increasingly crucial due to the emergence of a wide range of optimization problems. To tackle this issue, we present a new algorithm called Random Particle Swarm Optimization (RPSO) based on cosine similarity. RPSO is evaluated using both the IEEE Congress on Evolutionary Computation (CEC) 2022 test dataset and Convolutional Neural Network (CNN) classification experiments. The RPSO algorithm builds upon the traditional PSO algorithm by incorporating several key enhancements. Firstly, the parameter selection is adapted and a mechanism called Random Contrastive Interaction (RCI) is introduced. This mechanism fosters information exchange among particles, thereby improving the ability of the algorithm to explore the search space more effectively. Secondly, quadratic interpolation (QI) is incorporated to boost the local search efficiency of the algorithm. RPSO utilizes cosine similarity for the selection of both QI and RCI, dynamically updating population information to steer the algorithm towards optimal solutions. In the evaluation using the CEC 2022 test dataset, RPSO is compared with recent variations of Particle Swarm Optimization (PSO) and top algorithms in the CEC community. The results highlight the strong competitiveness and advantages of RPSO, validating its effectiveness in tackling global optimization tasks. Additionally, in the classification experiments with optimizing CNNs for medical images, RPSO demonstrated stability and accuracy comparable to other algorithms and variants. This further confirms the value and utility of RPSO in improving the performance of CNN classification tasks. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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38 pages, 15384 KiB  
Article
BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection
by Mengjun Li, Qifang Luo and Yongquan Zhou
Biomimetics 2024, 9(3), 187; https://doi.org/10.3390/biomimetics9030187 - 20 Mar 2024
Viewed by 882
Abstract
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and [...] Read more.
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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17 pages, 2894 KiB  
Article
Credit and Loan Approval Classification Using a Bio-Inspired Neural Network
by Spyridon D. Mourtas, Vasilios N. Katsikis, Predrag S. Stanimirović and Lev A. Kazakovtsev
Biomimetics 2024, 9(2), 120; https://doi.org/10.3390/biomimetics9020120 - 17 Feb 2024
Viewed by 1177
Abstract
Numerous people are applying for bank loans as a result of the banking industry’s expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry [...] Read more.
Numerous people are applying for bank loans as a result of the banking industry’s expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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18 pages, 2458 KiB  
Article
Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics
by Pravesh Kumar and Musrrat Ali
Biomimetics 2024, 9(2), 119; https://doi.org/10.3390/biomimetics9020119 - 16 Feb 2024
Viewed by 1082
Abstract
The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is [...] Read more.
The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as “Improved DE with Best and Worst positions (IDEBW)”, offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2024)
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