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Biomimetics
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Plant-Based Algorithm
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Plant-Based Algorithm

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

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 7988

Special Issue Editors

Dr. Nazmul Siddique

E-Mail Website
Guest Editor
School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, UK
Interests: nature-inspired computing; computational intelligence; machine learning
Dr. Anjan K. Ray

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, National Institute of Technology Sikkim, Ravangla 737139, Sikkim, India
Interests: principles of electrical engineering; control systems; advanced control systems; machine learning; robotics

Special Issue Information

Dear Colleagues,

Bio-inspired algorithms are based on notions of commonly observed behaviour and phenomena in animals, insects, and their movements. These include evolutionary algorithms, particle swarm optimisation, ant algorithms, bee algorithms, bat algorithms, firefly algorithms, and so on. Most bio-inspired algorithms simulate some behaviours and movements in animals, insects, and organisms. The common belief is that plants are sessile, incapable of physical movement, and do not show any kind of behaviour similar to animals. However, the mechanisms of plants’ sentience, survival, colonisation, branching strategies, flower pollination, and root foraging are fascinating behaviours quite different from those of animals and insects. Plants are as adept as animals and humans in reacting effectively to their ever-changing environment. Unfortunately, little attention has been given by researchers to plant behaviour-based algorithms. Plant-based algorithms are based on observed behaviour and phenomena seen in plants. This Special Issue solicits research papers on plant-based algorithms and their applications. The topics of interest include but are not limited to, plant-based algorithms, invasive-weed optimisation, flower pollination algorithm, plant growth optimisation, plant growth simulation algorithm, plant propagation algorithm, artificial plant optimisation, root foraging optimisation, and tree-seed algorithm.

Dr. Nazmul Siddique
Dr. Anjan K. Ray
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

  • plant-based algorithms
  • invasive weed optimisation
  • flower pollination algorithm
  • plant growth optimisation
  • plant growth simulation algorithm
  • plant propagation algorithm
  • artificial plant optimisation
  • root foraging optimisation
  • tree-seed algorithm

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Published Papers (4 papers)

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Research

44 pages, 8534 KiB  
Article
The Pine Cone Optimization Algorithm (PCOA)
by Mahdi Valikhan Anaraki and Saeed Farzin
Biomimetics 2024, 9(2), 91; https://doi.org/10.3390/biomimetics9020091 - 1 Feb 2024
Cited by 3 | Viewed by 1970
Abstract
The present study introduces a novel nature-inspired optimizer called the Pine Cone Optimization algorithm (PCOA) for solving science and engineering problems. PCOA is designed based on the different mechanisms of pine tree reproduction, including pollination and pine cone dispersal by gravity and animals. [...] Read more.
The present study introduces a novel nature-inspired optimizer called the Pine Cone Optimization algorithm (PCOA) for solving science and engineering problems. PCOA is designed based on the different mechanisms of pine tree reproduction, including pollination and pine cone dispersal by gravity and animals. It employs new and powerful operators to simulate the mentioned mechanisms. The performance of PCOA is analyzed using classic benchmark functions, CEC017 and CEC2019 as mathematical problems and CEC2006 and CEC2011 as engineering design problems. In terms of accuracy, the results show the superiority of PCOA to well-known algorithms (PSO, DE, and WOA) and new algorithms (AVOA, RW_GWO, HHO, and GBO). The results of PCOA are competitive with state-of-the-art algorithms (LSHADE and EBOwithCMAR). In terms of convergence speed and time complexity, the results of PCOA are reasonable. According to the Friedman test, PCOA’s rank is 1.68 and 9.42 percent better than EBOwithCMAR (second-best algorithm) and LSHADE (third-best algorithm), respectively. The authors recommend PCOA for science, engineering, and industrial societies for solving complex optimization problems. Full article
(This article belongs to the Special Issue Plant-Based Algorithm)
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30 pages, 7234 KiB  
Article
I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem
by Ayşe Beşkirli and İdiris Dağ
Biomimetics 2023, 8(8), 569; https://doi.org/10.3390/biomimetics8080569 - 27 Nov 2023
Cited by 13 | Viewed by 1800
Abstract
The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance [...] Read more.
The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules. Full article
(This article belongs to the Special Issue Plant-Based Algorithm)
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21 pages, 4960 KiB  
Article
Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm
by Mehmet Beşkirli and Mustafa Servet Kiran
Biomimetics 2023, 8(7), 540; https://doi.org/10.3390/biomimetics8070540 - 11 Nov 2023
Cited by 7 | Viewed by 1624
Abstract
Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals are permeable in a certain frequency range. Attenuation occurs in signals outside this frequency range. There are two types of filters: passive and active. Active filters consist of [...] Read more.
Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals are permeable in a certain frequency range. Attenuation occurs in signals outside this frequency range. There are two types of filters: passive and active. Active filters consist of passive and active components, including transistors and operational amplifiers, but also require a power supply. In contrast, passive filters only consist of resistors and capacitors. Therefore, active filters are capable of generating signal gain and possess the benefit of high-input and low-output impedance. In order for active filters to be more functional, the parameters of the resistors and capacitors in the circuit must be at optimum values. Therefore, the active filter is discussed in this study. In this study, the tree seed algorithm (TSA), a plant-based optimization algorithm, is used to optimize the parameters of filters with tenth-order Butterworth and Bessel topology. In order to improve the performance of the TSA for filter parameter optimization, opposition-based learning (OBL) is added to TSA to form an improved TSA (I-TSA). The results obtained are compared with both basic TSA and some algorithms. The experimental results show that the I-TSA method is applicable to this problem by performing a successful prediction process. Full article
(This article belongs to the Special Issue Plant-Based Algorithm)
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38 pages, 29598 KiB  
Article
A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems
by Yufei Yang and Changsheng Zhang
Biomimetics 2023, 8(2), 136; https://doi.org/10.3390/biomimetics8020136 - 26 Mar 2023
Cited by 4 | Viewed by 2005
Abstract
Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs [...] Read more.
Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the ϵ-constraint handling method, with the ϵ value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms. Full article
(This article belongs to the Special Issue Plant-Based Algorithm)
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