Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 227, P. 120367 - 120367
Published: May 6, 2023
Language: Английский
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(6)
Published: May 3, 2024
Abstract Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and engineering design problems are fundamental to the development of artificial intelligence. Traditional methods show limitations in dealing with these complex nonlinear models. To address challenges, swarm intelligence algorithm is introduced as a metaheuristic method effectively implemented. However, existing technology exhibits drawbacks such slow convergence speed, low precision, poor robustness. In this paper, we propose novel approach called Red-billed Blue Magpie Optimizer (RBMO), inspired by cooperative efficient predation behaviors red-billed blue magpies. The mathematical model RBMO was established simulating searching, chasing, attacking prey, food storage magpie. demonstrate RBMO’s performance, first conduct qualitative analyses through behavior experiments. Next, numerical optimization capabilities substantiated using CEC2014 (Dim = 10, 30, 50, 100) CEC2017 suites, consistently achieving best Friedman mean rank. UAV planning applications (two-dimensional three − dimensional), obtains preferable solutions, demonstrating its effectiveness solving NP-hard problems. Additionally, five problems, yields minimum cost, showcasing advantage practical problem-solving. We compare our experimental results categories widely recognized algorithms: (1) advanced variants, (2) recently proposed algorithms, (3) high-performance optimizers, including CEC winners.
Language: Английский
Citations
51
Materials Testing, Journal Year: 2024, Volume and Issue: 66(4), P. 544 - 552
Published: Jan. 24, 2024
Abstract Nature-inspired metaheuristic optimization algorithms have many applications and are more often studied than conventional techniques. This article uses the mountain gazelle optimizer, a recently created algorithm, artificial neural network to optimize mechanical components in relation vehicle component optimization. The family formation, territory-building, food-finding strategies of gazelles serve as major inspirations for algorithm. In order various engineering challenges, base algorithm (MGO) is hybridized with Nelder–Mead (HMGO-NM) current work. considered was applied solve four different categories, namely automobile, manufacturing, construction, tasks. Moreover, obtained results compared terms statistics well-known algorithms. findings show dominance over rest optimizers. being said HMGO can be common range industrial real-world problems.
Language: Английский
Citations
46
Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 295, P. 111737 - 111737
Published: April 12, 2024
This study proposes a novel artificial protozoa optimizer (APO) that is inspired by in nature. The APO mimics the survival mechanisms of simulating their foraging, dormancy, and reproductive behaviors. was mathematically modeled implemented to perform optimization processes metaheuristic algorithms. performance verified via experimental simulations compared with 32 state-of-the-art Wilcoxon signed-rank test performed for pairwise comparisons proposed algorithms, Friedman used multiple comparisons. First, tested using 12 functions 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, solve five popular engineering design problems continuous space constraints. Moreover, applied multilevel image segmentation task discrete experiments confirmed could provide highly competitive results problems. source codes Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.
Language: Английский
Citations
41
Advances in Engineering Software, Journal Year: 2024, Volume and Issue: 195, P. 103694 - 103694
Published: June 15, 2024
Language: Английский
Citations
40
Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 295, P. 111850 - 111850
Published: April 22, 2024
Language: Английский
Citations
32
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: Jan. 20, 2024
Language: Английский
Citations
26
Materials Testing, Journal Year: 2024, Volume and Issue: 66(7), P. 1063 - 1073
Published: April 30, 2024
Abstract In this article, a newly developed optimization approach based on mathematics technique named the geometric mean algorithm is employed to address challenge of robot gripper, airplane bracket, and suspension arm automobiles, followed by an additional three engineering problems. Accordingly, other challenges are ten-bar truss, three-bar tubular column, spring systems. As result, demonstrates promising statistical outcomes when compared well-established algorithms. Additionally, it requires less iteration achieve global optimum solution. Furthermore, exhibits minimal deviations in results, even techniques produce better or similar outcomes. This suggests that proposed paper can be effectively utilized for wide range critical industrial real-world challenges.
Language: Английский
Citations
21
Materials Testing, Journal Year: 2024, Volume and Issue: 66(8), P. 1230 - 1240
Published: July 5, 2024
Abstract This paper introduces a novel approach, the Modified Electric Eel Foraging Optimization (EELFO) algorithm, which integrates artificial neural networks (ANNs) with metaheuristic algorithms for solving multidisciplinary design problems efficiently. Inspired by foraging behavior of electric eels, algorithm incorporates four key phases: interactions, resting, hunting, and migrating. Mathematical formulations each phase are provided, enabling to explore exploit solution spaces effectively. The algorithm’s performance is evaluated on various real-world optimization problems, including weight engineering components, economic pressure handling vessels, cost welded beams. Comparative analyses demonstrate superiority MEELFO in achieving optimal solutions minimal deviations computational effort compared existing methods.
Language: Английский
Citations
21
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: Feb. 11, 2024
Abstract The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this due their potential approximate the optimal solution for several complicated problems. Despite that, existing still suffer from sluggish convergence rates and stagnation in local optima when applied tackle problem. study presents new estimation technique, namely HKOA, based on integrating published Kepler algorithm (KOA) with ranking-based update exploitation improvement mechanisms estimate unknown parameters third-, single-, double-diode models. former mechanism aims at promoting KOA’s exploration operator diminish getting stuck optima, while latter strengthen its faster converge solution. Both KOA HKOA are validated using RTC France solar cell five PV modules, including Photowatt-PWP201, Ultra 85-P, STP6-120/36, STM6-40/36, show efficiency stability. In addition, they extensively compared techniques effectiveness. According experimental findings, strong alternative method estimating because it can yield substantially different superior findings
Language: Английский
Citations
20
The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(9), P. 12346 - 12407
Published: Feb. 12, 2024
Language: Английский
Citations
19