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Nature-Inspired Metaheuristic Optimization Algorithms

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 15581

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Special Issue Editors

Dr. Ameer Hamza Khan

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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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shuai Li

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Guest Editor

Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Interests: robotic manipulation; autonomous manufacturing; multi-robot coordination; intelligent control and optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Danish Hussain

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Guest Editor

College of Electrical & Mechanical Engineering, National University of Sciences & Technology (NUST), Islamabad, Pakistan
Interests: micro and nano robotics; AFM imaging; mobile robotics; nano materials
Special Issues, Collections and Topics in MDPI journals

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 a huge 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 (11 papers)

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Research

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24 pages, 3858 KiB

Open AccessArticle

Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems

by Hazem Hassan Ellithy, Hany M. Hasanien, Mohammed Alharbi, Mohamed A. Sobhy, Adel M. Taha and Mahmoud A. Attia

Biomimetics 2024, 9(2), 66; https://doi.org/10.3390/biomimetics9020066 - 23 Jan 2024

Cited by 1 | Viewed by 986

Abstract

Photovoltaic (PV) systems are becoming essential to our energy landscape as renewable energy sources become more widely integrated into power networks. Preserving grid stability, especially during voltage sags, is one of the significant difficulties confronting the implementation of these technologies. This attribute is referred to as low-voltage ride-through (LVRT). To overcome this issue, adopting a Proportional-Integral (PI) controller, a control system standard, is proving to be an efficient solution. This paper provides a unique algorithm-based approach of the Marine Predator Algorithm (MPA) for optimized tuning of the used PI controller, mainly focusing on inverter control, to improve the LVRT of the grid, leading to improvements in the overshoot, undershoot, settling time, and steady-state response of the system. The fitness function is optimized using the MPA to determine the settings of the PI controller. This process helps to optimally design the controllers optimally, thus improving the inverter control and performance and enhancing the system’s LVRT capability. The methodology is tested in case of a 3L-G fault. To test its validity, the proposed approach is compared with rival standard optimization-based PI controllers, namely Grey Wolf Optimization and Particle Swarm Optimization. The comparison shows that the used algorithm provides better results with a higher convergence rate with overshoot ranging from 14% to 40% less in the case of DC-Link Voltage and active power and also settling times in the case of MPA being less than PSO and GWO by 0.76 to 0.95 s. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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54 pages, 13728 KiB

Open AccessArticle

Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

by Osama Al-Baik, Saleh Alomari, Omar Alssayed, Saikat Gochhait, Irina Leonova, Uma Dutta, Om Parkash Malik, Zeinab Montazeri and Mohammad Dehghani

Biomimetics 2024, 9(2), 65; https://doi.org/10.3390/biomimetics9020065 - 23 Jan 2024

Cited by 1 | Viewed by 1879

Abstract

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator’s attack on a pufferfish and (ii) exploitation based on the simulation of a predator’s escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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20 pages, 4139 KiB

Open AccessArticle

Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach

by Thi-Kien Dao, Truong-Giang Ngo, Jeng-Shyang Pan, Thi-Thanh-Tan Nguyen and Trong-The Nguyen

Biomimetics 2024, 9(1), 35; https://doi.org/10.3390/biomimetics9010035 - 5 Jan 2024

Cited by 1 | Viewed by 1249

Abstract

Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach’s effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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38 pages, 4297 KiB

Open AccessArticle

Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design

by Jiaxu Huang and Haiqing Hu

Biomimetics 2024, 9(1), 21; https://doi.org/10.3390/biomimetics9010021 - 2 Jan 2024

Viewed by 1016

Abstract

In this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a dynamic opposite learning strategy broadens the search area of the population, enhances global search ability, and improves population diversity. In the honey harvesting stage of the honey badger (development), differential mutation strategies are combined, selectively introducing local quantum search strategies that enhance local search capabilities and improve population optimization accuracy, or introducing dynamic Laplacian crossover operators that can improve convergence speed, while reducing the odds of the HBA sinking into local optima. Through comparative experiments with other algorithms on the CEC2017, CEC2020, and CEC2022 test sets, and three engineering examples, EHBA has been verified to have good solving performance. From the comparative analysis of convergence graphs, box plots, and algorithm performance tests, it can be seen that compared with the other eight algorithms, EHBA has better results, significantly improving its optimization ability and convergence speed, and has good application prospects in the field of optimization problems. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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18 pages, 3535 KiB

Open AccessArticle

Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis

by Donwoo Lee, Seungjae Lee and Sudeok Shon

Biomimetics 2024, 9(1), 11; https://doi.org/10.3390/biomimetics9010011 - 27 Dec 2023

Viewed by 1054

Abstract

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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23 pages, 3427 KiB

Open AccessArticle

Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization

by Sultan Hassan Hakmi, Abdullah M. Shaheen, Hashim Alnami, Ghareeb Moustafa and Ahmed Ginidi

Biomimetics 2023, 8(8), 608; https://doi.org/10.3390/biomimetics8080608 - 14 Dec 2023

Cited by 1 | Viewed by 1180

Abstract

Combined Heat and Power Units Economic Dispatch (CHPUED) is a challenging non-convex optimization challenge in the power system that aims at decreasing the production cost by scheduling the heat and power generation outputs to dedicated units. In this article, a Kepler optimization algorithm (KOA) is designed and employed to handle the CHPUED issue under valve points impacts in large-scale systems. The proposed KOA is used to forecast the position and motion of planets at any given time based on Kepler’s principles of planetary motion. The large 48-unit, 96-unit, and 192-unit systems are considered in this study to manifest the superiority of the developed KOA, which reduces the fuel costs to 116,650.0870 USD/h, 234,285.2584 USD/h, and 487,145.2000 USD/h, respectively. Moreover, the dwarf mongoose optimization algorithm (DMOA), the energy valley optimizer (EVO), gray wolf optimization (GWO), and particle swarm optimization (PSO) are studied in this article in a comparative manner with the KOA when considering the 192-unit test system. For this large-scale system, the presented KOA successfully achieves improvements of 19.43%, 17.49%, 39.19%, and 62.83% compared to the DMOA, the EVO, GWO, and PSO, respectively. Furthermore, a feasibility study is conducted for the 192-unit test system, which demonstrates the superiority and robustness of the proposed KOA in obtaining all operating points between the boundaries without any violations. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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26 pages, 2091 KiB

Open AccessArticle

Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs

by Junjie Tang and Lianguo Wang

Biomimetics 2023, 8(8), 576; https://doi.org/10.3390/biomimetics8080576 - 1 Dec 2023

Viewed by 1069

Abstract

To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorithm was improved by combining the search strategy of the sine cosine algorithm, the m-neighborhood locally optimal individual-guided search strategy, and the global optimal individual-guided search strategy, and, then, the three search strategies were executed alternately, which achieved collaboration, improved the convergence accuracy, and prevented the algorithm from falling into local optima. Finally, a greedy selection strategy was employed to select the best individuals for the population, which accelerated the convergence speed of the sine cosine algorithm. The simulation results illustrated that the sine cosine algorithm for elite individual collaborative search demonstrated a better optimization performance than the sine cosine algorithm, the other improved sine cosine algorithms, the other chaos-based algorithms, and other intelligent optimization algorithms. In addition, the feasibility and applicability of the sine cosine algorithm for elite individual collaborative search were further demonstrated by two mechanical optimization design experiments. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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23 pages, 5472 KiB

Open AccessArticle

Evolutionary Computing for the Radiative–Convective Heat Transfer of a Wetted Wavy Fin Using a Genetic Algorithm-Based Neural Network

by B. S. Poornima, Ioannis E. Sarris, K. Chandan, K.V. Nagaraja, R. S. Varun Kumar and Samia Ben Ahmed

Biomimetics 2023, 8(8), 574; https://doi.org/10.3390/biomimetics8080574 - 1 Dec 2023

Cited by 10 | Viewed by 1412

Abstract

Evolutionary algorithms are a large class of optimization techniques inspired by the ideas of natural selection, and can be employed to address challenging problems. These algorithms iteratively evolve populations using crossover, which combines genetic information from two parent solutions, and mutation, which adds random changes. This iterative process tends to produce effective solutions. Inspired by this, the current study presents the results of thermal variation on the surface of a wetted wavy fin using a genetic algorithm in the context of parameter estimation for artificial neural network models. The physical features of convective and radiative heat transfer during wet surface conditions are also considered to develop the model. The highly nonlinear governing ordinary differential equation of the proposed fin problem is transmuted into a dimensionless equation. The graphical outcomes of the aspects of the thermal profile are demonstrated for specific non-dimensional variables. The primary observation of the current study is a decrease in temperature profile with a rise in wet parameters and convective-conductive parameters. The implemented genetic algorithm offers a powerful optimization technique that can effectively tune the parameters of the artificial neural network, leading to an enhanced predictive accuracy and convergence with the numerically obtained solution. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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21 pages, 1238 KiB

Open AccessEditor’s ChoiceArticle

A Dual-Layer Weight-Leader-Vicsek Model for Multi-AGV Path Planning in Warehouse

by Shiwei Lin, Ang Liu and Jianguo Wang

Biomimetics 2023, 8(7), 549; https://doi.org/10.3390/biomimetics8070549 - 15 Nov 2023

Viewed by 1247

Abstract

Multiple automatic guided vehicles are widely involved in industrial intelligence. Path planning is crucial for their successful application. However, achieving robust and efficient path planning of multiple automatic guided vehicles for real-time implementation is challenging. In this paper, we propose a two-layer strategy for multi-vehicle path planning. The approach aims to provide fast computation and operation efficiency for implementation. The start–destination matrix groups all the vehicles, generating a dynamic virtual leader for each group. In the first layer, the hybrid A* algorithm is employed for the path planning of the virtual leaders. The second layer is named leader–follower; the proposed Weight-Leader-Vicsek model is applied to navigate the vehicles following their virtual leaders. The proposed method can reduce computational load and achieve real-time navigation by quickly updating the grouped vehicles’ status. Collision and deadlock avoidance is also conducted in this model. Vehicles in different groups are treated as dynamic obstacles. We validated the method by conducted simulations through MATLAB to verify its path-planning functionality and experimented with a localization sensor. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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Graphical abstract

36 pages, 6667 KiB

Open AccessArticle

A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems Based on Technical and Vocational Education and Training

by Marie Hubalovska and Stepan Major

Biomimetics 2023, 8(6), 508; https://doi.org/10.3390/biomimetics8060508 - 23 Oct 2023

Cited by 1 | Viewed by 1539

Abstract

In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational education and training schools. The theory of TVETBO is expressed and mathematically modeled in three phases: (i) theory education, (ii) practical education, and (iii) individual skills development. The performance of TVETBO when solving optimization problems is evaluated on the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that TVETBO, with its high abilities to explore, exploit, and create a balance between exploration and exploitation during the search process, is able to provide effective solutions for the benchmark functions. The results obtained from TVETBO are compared with the performances of twelve well-known metaheuristic algorithms. A comparison of the simulation results and statistical analysis shows that the proposed TVETBO approach provides better results in most of the benchmark functions and provides a superior performance in competition with competitor algorithms. Furthermore, in order to measure the effectiveness of the proposed approach in dealing with real-world applications, TVETBO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite. The simulation results show that TVETBO provides an effective and superior performance when solving constrained optimization problems of real-world applications compared to competitor algorithms. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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Other

Jump to: Research

50 pages, 3654 KiB

Open AccessSystematic Review

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

by José Barrera-García, Felipe Cisternas-Caneo, Broderick Crawford, Mariam Gómez Sánchez and Ricardo Soto

Biomimetics 2024, 9(1), 9; https://doi.org/10.3390/biomimetics9010009 - 25 Dec 2023

Cited by 1 | Viewed by 1407

Abstract

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)

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