Machine intelligence in metamaterials design: a review

Oxford Open Materials Science, Journal Year: 2024, Volume and Issue: 4(1)

Published: Jan. 1, 2024

Abstract Machine intelligence continues to rise in popularity as an aid the design and discovery of novel metamaterials. The properties metamaterials are essentially controllable via their architectures until recently, process has relied on a combination trial-and-error physics-based methods for optimization. These processes can be time-consuming challenging, especially if space metamaterial optimization is explored thoroughly. Artificial (AI) machine learning (ML) used overcome challenges like these pre-processed massive datasets very accurately train appropriate models. models broad, describing properties, structure, function at numerous levels hierarchy, using relevant inputted knowledge. Here, we present comprehensive review literature where state-of-the-art design, development In this review, individual approaches categorized based methodology application. We further trends over wide range problems including: acoustics, photonics, plasmonics, mechanics, more. Finally, identify discuss recent research directions highlight current gaps

Language: Английский

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An Improved Bio-Inspired Material Generation Algorithm for Engineering Optimization Problems Including PV Source Penetration in Distribution Systems

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 603 - 603

Published: Jan. 9, 2025

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore refine parameter space. By simulating bonding processes—such as ionic covalent bonds—MGO generates new solution candidates evaluates their stability, guiding toward convergence on optimal values. To improve its search efficiency, this paper introduces Enhanced (IMGO) algorithm, integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct compounds, resulting in increased diversity, more thorough exploration space, improved resistance local optima. adaptable non-linear adjustments QILP’s quadratic function allow traverse complex landscapes effectively. This IMGO, along with original MGO, developed support applications across phases, showcasing versatility enhanced optimization capabilities. Initially, both MGO algorithms are evaluated using several mathematical benchmarks from CEC 2017 test suite measure Following this, applied following well-known engineering problems: welded beam design, rolling element bearing pressure vessel design. simulation results then compared various established bio-inspired algorithms, including Artificial Ecosystem (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Algorithm (CBOA), Beluga Whale (BWOA), Arithmetic-Trigonometric (ATOA), Atomic Orbital Searching (AOSA). Moreover, IMGO tested real Egyptian power distribution system optimize placement PV capacitor units aim minimizing energy losses. Lastly, parameters estimation problem successfully solved via considering commercial RTC France cell. Comparative studies demonstrate that not only achieves significant loss reduction but also contributes environmental sustainability reducing emissions, overall effectiveness practical applications. outcomes 23 benchmark models average accuracy enhancement 65.22% consistency 69.57% method. Also, application achieved computational errors 27.8% while maintaining superior stability alternative methods.

Language: Английский

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Newton Raphson Based Optimizer for Optimal Integration of FAS and RIS in Wireless Systems

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103822 - 103822

Published: Jan. 1, 2025

Language: Английский

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

Biomimetics, Journal Year: 2023, Volume and Issue: 8(8), P. 619 - 619

Published: Dec. 17, 2023

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in wild. The fundamental inspiration design GAO derived from hunting strategy armadillos moving towards prey positions and digging termite mounds. theory expressed mathematically modeled two phases: (i) exploration based on simulating movement mounds, (ii) exploitation armadillos' skills order to rip open performance handling optimization tasks evaluated solve CEC 2017 test suite for problem dimensions equal 10, 30, 50, 100. results show that able achieve effective solutions problems by benefiting its high abilities exploration, exploitation, balancing them during search process. quality obtained compared with twelve well-known algorithms. simulation presents superior competitor algorithms providing better most benchmark functions. statistical analysis Wilcoxon rank sum confirms has significant superiority over implementation 2011 four engineering proposed approach dealing real-world applications.

Language: Английский

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A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems

Biomimetics, Journal Year: 2023, Volume and Issue: 8(4), P. 332 - 332

Published: July 27, 2023

The present study introduces a subtraction-average-based optimization algorithm (SAOA), unique enhanced evolutionary technique for solving engineering problems. typical SAOA works by subtracting the average of searcher agents from position population members in search space. To increase searching capabilities, this proposes an improved SAO (ISAO) that incorporates cooperative learning based on leader solution. First, after considering testing different standard mathematical benchmark functions, proposed ISAOA is assessed comparison to SAOA. simulation results declare establishes great superiority over Additionally, adopted handle power system applications Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction electrical grids. and are employed optimally size TCSCs simultaneously select their installed transmission lines. Both compared two recent algorithms, Artificial Ecosystem Optimizer (AEO) AQuila Algorithm (AQA), other effective well-known Grey Wolf (GWO) Particle Swarm (PSO). In three separate case studies, IEEE-30 bus used purpose while varying numbers TCSC devices will be deployed. suggested ISAOA's simulated implementations claim significant loss reductions analyzed situations GWO, AEO, PSO, AQA.

Language: Английский

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An Advanced Bio-Inspired Mantis Search Algorithm for Characterization of PV Panel and Global Optimization of Its Model Parameters

Biomimetics, Journal Year: 2023, Volume and Issue: 8(6), P. 490 - 490

Published: Oct. 18, 2023

Correct modelling and estimation of solar cell characteristics are crucial for effective performance simulations PV panels, necessitating the development creative approaches to improve energy conversion. When handling this complex problem, traditional optimisation algorithms have significant disadvantages, including a predisposition get trapped in certain local optima. This paper develops Mantis Search Algorithm (MSA), which draws inspiration from unique foraging behaviours sexual cannibalism praying mantises. The suggested MSA includes three stages optimisation: prey pursuit, assault, cannibalism. It is created R.TC France Ultra 85-P panel related Shell PowerMax calculating parameters examining six case studies utilising one-diode model (1DM), two-diode three-diode (3DM). Its assessed contrast recently developed optimisers neural network algorithm (NNA), dwarf mongoose (DMO), zebra (ZOA). In light adopted approach, simulation findings electrical power systems. methodology improves 1DM, 2DM, 3DM by 12.4%, 44.05%, 48.88%, 28.96%, 43.19%, 55.81%, 37.71%, 32.71%, 60.13% relative DMO, NNA, ZOA approaches, respectively. For panel, designed technique achieves improvements 62.05%, 67.14%, 84.25%, 49.05%, 53.57%, 74.95%, 37.03%, 37.4%, 59.57% compared techniques,

Language: Английский

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Improving Safety and Efficiency of Industrial Vehicles by Bio‐Inspired Algorithms

Expert Systems, Journal Year: 2025, Volume and Issue: 42(3)

Published: Jan. 22, 2025

ABSTRACT In the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing operational efficiency and safety. They must travel in crowded work areas cross narrow corridors with strict safety time requirements. Bio‐inspired optimization algorithms have emerged as a promising approach to deal complex scenarios. Thus, this paper explores ability three novel bio‐inspired algorithms: Bat Algorithm (BA), Whale Optimization (WOA) Gazelle (GOA); optimise AGV path planning environments. To do it, new strategy described: trajectory based on clothoid curves specialised piece‐wise fitness function which prioritises designed. Simulation experiments were conducted across different occupancy maps evaluate performance each algorithm. WOA demonstrates faster providing suitable solutions 4 times than GOA. Meanwhile, GOA gives better metrics but demands more computational time. The study highlights potential approaches optimisation suggests avenues future research, including hybrid algorithm development.

Language: Английский

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A Comprehensive Survey on Seagull Optimization Algorithm and Its Variants

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Blending traditional and novel techniques: Hybrid type-1 fuzzy functions for forecasting

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110445 - 110445

Published: March 6, 2025

Language: Английский

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Power Tracking and Performance Analysis of Hybrid Perturb–Observe, Particle Swarm Optimization, and Fuzzy Logic-Based Improved MPPT Control for Standalone PV System

Technologies, Journal Year: 2025, Volume and Issue: 13(3), P. 112 - 112

Published: March 8, 2025

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable sources. Photovoltaic (PV) systems are pivotal in converting solar into electricity a significant role sustainable production. Therefore, it is critical implement maximum power point tracking (MPPT) controllers optimize efficiency PV by extracting accessible power. This research investigates performance comparison various MPPT control algorithms for standalone system. Several cases involving individual controllers, as well hybrid combinations using two three been simulated MATLAB/SIMULINK. sensed parameters, i.e., output power, voltage, current, specify that though effectively track point, achieve superior utilizing combined strengths each algorithm. results indicate such perturb observe (P&O), particle swarm optimization (PSO), fuzzy logic (FL), achieved efficiencies 97.6%, 90.3%, 90.1%, respectively. In contrast, dual P&O-PSO, PSO-FL, P&O-FL demonstrated improved performance, with 96.8%, 96.4%, 96.5%, also proposes new triple-MPPT controller combining P&O-PSO-FL, which surpassed both dual-hybrid achieving an impressive 99.5%. Finally, all seven presented, highlighting advantages disadvantages approaches.

Language: Английский

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