Cluster Computing, Год журнала: 2024, Номер 27(10), С. 14469 - 14514
Опубликована: Июль 21, 2024
Язык: Английский
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Апрель 1, 2024
Abstract This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon Marine Predators Algorithm (MPA) to enhance search capabilities of (GTO). Like numerous other algorithms, GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate adaptable optimization problems, especially compared more techniques. Addressing these challenges aiming for improved performance, this paper proposes EGTO, integrating high low-velocity ratios inspired by MPA. The EGTO technique effectively balances exploration exploitation phases, achieving impressive results utilizing fewer parameters operations. Evaluation on a diverse array benchmark functions, comprising 23 established functions ten complex ones from CEC2019 benchmark, highlights its performance. Comparative analysis against techniques reveals EGTO's superiority, consistently outperforming counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based artificial rabbits algorithm, pelican Runge Kutta algorithm (RUN), original algorithms across various test functions. Furthermore, efficacy extends addressing seven challenging engineering design encompassing three-bar truss design, compression spring pressure vessel cantilever beam welded speed reducer gear train design. showcase robust rate, adeptness locating local/global optima, supremacy over alternative methodologies explored.
Язык: Английский
Процитировано
6
IEEE Access, Год журнала: 2023, Номер 11, С. 62630 - 62638
Опубликована: Янв. 1, 2023
To address the problems of slow convergence speed, easy to fall into local minima and low accuracy presented by previous algorithms in DC distribution network fault location, this paper adopts improved artificial bee colony slime mould algorithm (SMA) improve solve. On basis SMA, an adaptive adjustable feedback factor crossover operator are introduced speed; (ABC) is search ability jump out minima, (ISMA) formed. Firstly, based on six-terminal topology, a mathematical model bipolar short-circuit as well single-pole grounded established occurring between G-VSC W-VSC example. Then principle ISMA detail, suitable fitness function measure location network. Finally, experimental simulations conducted obtain points from optimization compare them with actual values verify algorithm. In addition, efficiency robustness further verified comparing other algorithms.
Язык: Английский
Процитировано
12
Journal of Engineering and Applied Science, Год журнала: 2024, Номер 71(1)
Опубликована: Янв. 3, 2024
Abstract High-performance concrete (HPC) is commonly utilized in the construction industry because of its strength and durability. The mechanical properties HPC, specifically compressive tensile strength, are crucial indicators. Accurate prediction for optimizing design as well performance structures. In this investigation, a novel approach HPC proposed, employing Support Vector Regression (SVR) algorithm conjunction with three optimizers: Slime Mold Algorithm (SMA), Adaptive Opposition (AOSM), Equilibrium (ESMA). SVR robust machine-learning technique that has displayed promising results various tasks. utilization allows effective modeling complex relationship between influencing factors. To achieve this, dataset comprising 344 samples high-performance was collected to train assess algorithm. However, choice suitable optimization algorithms becomes enhance accuracy convergence speed. Through extensive experimentation comparative analysis, proposed framework’s evaluated using real-world data. demonstrate combining AOSM, ESMA, SMA outperforms traditional speed methods. suggested framework provides an reliable solution accurately predicting (CS) enabling engineers researchers optimize processes
Язык: Английский
Процитировано
4
PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2688 - e2688
Опубликована: Фев. 11, 2025
Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple tasks. Addressing issues such as limited precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake (MTSO) algorithm. The MTSO algorithm operates two phases: first, independently handling each problem; second, transferring knowledge. Knowledge transfer is determined by the probability of knowledge selection elite individuals. Based on decision, either transfers from other tasks or updates current task through self-perturbation. Experimental results indicate that, compared advanced proposed achieves most accurate solutions multitask benchmark functions, five-task 10-task planar kinematic arm control problems, robot gripper problem, car side-impact design problem. code data for can be obtained from: https://doi.org/10.5281/zenodo.14197420.
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Март 27, 2025
Abstract Enterprise Development Optimizer (EDO) is a meta-heuristic algorithm inspired by the enterprise development process with strong global search capability. However, analysis of EDO shows that it suffers from defects rapidly decreasing population diversity and weak exploitation ability when dealing complex optimization problems, while its algorithmic structure has room for further enhancement in process. In order to solve these challenges, this paper proposes multi-strategy optimizer called MSEDO based on basic EDO. A leader-based covariance learning strategy proposed, aiming strengthen quality agents alleviate later stage through guiding role dominant group modifying leader. To dynamically improve local capability algorithm, fitness distance-based leader selection proposed. addition, reconstructed diversity-based restart presented. The utilized assist jump out optimum stuck stagnation. Ablation experiments verify effectiveness strategies algorithm. performance confirmed comparing five different types improved metaheuristic algorithms. experimental results CEC2017 CEC2022 show effective escaping optimums favorable exploration capabilities. ten engineering constrained problems competently real-world problems.
Язык: Английский
Процитировано
0
DELETED, Год журнала: 2025, Номер unknown
Опубликована: Март 29, 2025
Язык: Английский
Процитировано
0
Biomimetics, Год журнала: 2025, Номер 10(5), С. 260 - 260
Опубликована: Апрель 23, 2025
In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced efficiency, hindrance comprehensive exploration the solution space. To address shortcomings DCS algorithm, this paper proposes multi-strategy (MSDCS) based on collaborative development mechanism evaluation strategy. First, that organically integrates estimation distribution to compensate for algorithm’s insufficient ability its tendency fall into local optimums through guiding effect dominant populations, improve quality efficiency at same time. Secondly, new strategy realize coordinated transition between exploitation fitness distance. Finally, linear size reduction incorporated DCS, which significantly improves overall performance by maintaining large initial stage enhance capability extensive space, then gradually decreasing later capability. A series validations was conducted CEC2018 test set, experimental results were analyzed using Friedman Wilcoxon rank sum test. show superior MSDCS terms convergence speed, stability, global optimization. addition, successfully applied several constrained problems. all cases, outperforms basic fast strong robustness, emphasizing efficacy practical applications.
Язык: Английский
Процитировано
0
Journal of Computational Design and Engineering, Год журнала: 2023, Номер 10(6), С. 2122 - 2146
Опубликована: Окт. 17, 2023
Abstract Sand cat swarm optimization (SCSO) is a recently introduced popular intelligence metaheuristic algorithm, which has two significant limitations – low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on arithmetic algorithm (AOA), refracted opposition-based learning crisscross strategy, called sand (SC-AOA), AOA balance exploration exploitation reduce possibility of falling into optimum, used strategy enhance accuracy. The effectiveness SC-AOA benchmarked 10 benchmark functions, CEC 2014, 2017, 2022, eight engineering problems. results show that competitive performance.
Язык: Английский
Процитировано
9
Mathematical Biosciences & Engineering, Год журнала: 2024, Номер 21(2), С. 2856 - 2878
Опубликована: Янв. 1, 2024
<abstract> <p>Three-dimensional path planning refers to determining an optimal in a three-dimensional space with obstacles, so that the is as close target location possible, while meeting some other constraints, including distance, altitude, threat area, flight time, energy consumption, and on. Although bald eagle search algorithm has characteristics of simplicity, few control parameters, strong global capabilities, it not yet been applied complex problems. In order broaden application scenarios scope solve problem space, we present study where five geographical environments are simulated represent real-life unmanned aerial vehicles flying scenarios. These maps effectively test algorithm's ability handle various terrains, extreme environments. The experimental results have verified excellent performance BES algorithm, which can quickly, stably, problems, making highly competitive this field.</p> </abstract>
Язык: Английский
Процитировано
3
Artificial Intelligence Review, Год журнала: 2024, Номер 57(7)
Опубликована: Июнь 6, 2024
Abstract Harmony Search (HS) algorithm is a swarm intelligence inspired by musical improvisation. Although HS has been applied to various engineering problems, it faces challenges such as getting trapped in local optima, slow convergence speed, and low optimization accuracy when complex problems. To address these issues, this paper proposes an improved version of called Equilibrium Optimization-based Algorithm with Nonlinear Dynamic Domains (EO-HS-NDD). EO-HS-NDD integrates multiple leadership-guided strategies from the Optimizer (EO) algorithm, using harmony memory considering disharmony historical memory, while leveraging hidden guidance direction information Optimizer. Additionally, designs nonlinear dynamic domain adaptively adjust search space size accelerate speed. Furthermore, balance exploration exploitation capabilities, appropriate adaptive adjustments are made Memory Considering Rate (HMCR) Pitch Adjustment (PAR). Experimental validation on CEC2017 test function set demonstrates that outperforms nine other variants terms robustness, accuracy. Comparisons advanced versions Differential Evolution (DE) also indicate exhibits superior solving capabilities. Moreover, solve 15 real-world problems CEC2020 compared algorithms competition. The experimental results show performs well
Язык: Английский
Процитировано
3