Review of nature and biologically inspired metaheuristics for greenhouse environment control

Oliveira, P. B. de Moura; Pires, E. J. Solteiro; Boaventura‐Cunha, José; Pinho, Tatiana M.

doi:10.1177/0142331220909010

Cited by 22 publications

(8 citation statements)

References 106 publications

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“…Bio-inspired optimization algorithm represents a class of metaheuristic algorithms whose principles are inspired by biology and natural phenomenon and have been successfully applied to solve different problems [49]. This category of algorithms exploits the basic process of nature and then translates them into rules or procedures, which are then model computationally for solving complex real-life problems [50] [51] [52], [53], [54], [55], [56], [57].…”

Section: Metaheuristic Optimization Algorithms: Bioinspired-based Algorithmsmentioning

confidence: 99%

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

et al. 2022

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Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola optimization search algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), exposed (E), Infected (I), Hospitalized (H), recovered (R), Vaccinated (V), Quarantine (Q), and Death or death (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%.

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Section: Metaheuristic Optimization Algorithms: Bioinspired-based Algorithmsmentioning

confidence: 99%

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

et al. 2022

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“…The design of greenhouse structures and ventilation is important to control the internal micro-climate, especially the temperature and the humidity, among other parameters, that predict crop growth and yield [ 155 , 178 , 179 , 180 ]. Bio-inspired methods of complex branching, metaheuristics, and streamlining have been described to optimize ventilation and sunlight in a greenhouse [ 181 ]. Anthill mounds have been examined to design a natural ventilation system for greenhouses and bricks [ 173 ].…”

Section: Biomimetic Materials Structures and Resource Managementmentioning

confidence: 99%

Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

et al. 2022

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Biomimetics is the interdisciplinary cooperation of biology and technology that offers solutions to practical problems by analyzing biological systems and transferring their principles into applications. This review article focused on biomimetic innovations, including bio-inspired soft robots and swarm robots that could serve multiple functions, including the harvesting of fruits, pest control, and crop management. The research demonstrated commercially available biomimetic innovations, including robot bees by Arugga AI Farming and the Robotriks Traction Unit (RTU) precision farming equipment. Additionally, soft robotic systems have made it possible to mitigate the risk of surface bruises, rupture, the crushing destruction of plant tissue, and plastic deformation in the harvesting of fruits with a soft rind such as apples, cherries, pears, stone fruits, kiwifruit, mandarins, cucumbers, peaches, and pome. Even though the smart farming technologies, which were developed to mimic nature, could help prevent climate change and enhance the intensification of agriculture, there are concerns about long-term ecological impact, cost, and their inability to complement natural processes such as pollination. Despite the problems, the market for bio-inspired technologies with potential agricultural applications to modernize farming and solve the abovementioned challenges has increased exponentially. Future research and development should lead to low-cost FEA robotic grippers and FEA-tendon-driven grippers for crop harvesting. In brief, soft robots and swarm robotics have immense potential in agriculture.

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“…Regardless of the model order, these algorithms serve as a critical tool for developing classical and modified structured controllers for an unstable process model class. The genetic algorithm (GA) [37] and particle swarm optimization (PSO) technique [38] are the two key strategies commonly used in controller design applications for optimization. Due to the intensive study of various algorithms, the PSO technique has significantly been improved for numerous industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Voltage Stability Enhancement in Electric Vehicle Battery Charger Using Particle Swarm Optimization

et al. 2022

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Electric vehicles (EVs) are poised to lead the transportation sector as the primary choice of automobile due to their efficiency and environmental benefits. EVs with enhanced autonomy and reduced pricing have become feasible in the market, enabling a gradual transition for higher EV penetration. However, electric vehicles require highly efficient and stabilized charging stations in urban areas to ensure the vehicle's charging time is not compromised. In this regard, the Vienna rectifier with a voltage-oriented controller (VOC) plays a significant role in improving the power quality of the utility grid for EV battery charger applications. The low stability of the battery charger's output voltage and current is due to the trial-and-error method used to select the PI controller gains. In order to improve the voltage and current stability, the particle swarm optimization (PSO) technique is used to optimize VOC's PI controller gains. The code composer studio (CCS) platform integrates the PSO technique for EV battery chargers in the experimental setup. The Vienna rectifier with VOC for EV battery charger is implemented using TMS 320F28337 digital signal controller in the test board. Findings indicate that the PSO optimized VOC improves the output voltage and current stability by 12% compared to the existing trial-anderror technique. Furthermore, the proposed system is tested in an experimental setup that provides input current THD to less than 5% for different load variations (up to 1.5kW) to meet the IEEE-519 standards. Results from simulations and experimental setup verify that the proposed PSO-PI controller-based Vienna rectifier significantly improves EV battery chargers' output voltage and current stability.INTEX TERMS Vienna rectifier, voltage oriented controller (VOC), particle swarm optimization (PSO), electric vehicles, charging stations

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Review of nature and biologically inspired metaheuristics for greenhouse environment control

Cited by 22 publications

References 106 publications

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

Dynamic Voltage Stability Enhancement in Electric Vehicle Battery Charger Using Particle Swarm Optimization

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