A New Hybrid Improved Arithmetic Optimization Algorithm for Solving Global and Engineering Optimization Problems DOI Creative Commons

Yalong Zhang,

Lining Xing

Mathematics, Journal Year: 2024, Volume and Issue: 12(20), P. 3221 - 3221

Published: Oct. 14, 2024

The Arithmetic Optimization Algorithm (AOA) is a novel metaheuristic inspired by mathematical arithmetic operators. Due to its simple structure and flexible parameter adjustment, the AOA has been applied solve various engineering problems. However, still faces challenges such as poor exploitation ability tendency fall into local optima, especially in complex, high-dimensional In this paper, we propose Hybrid Improved (HIAOA) address issues of susceptibility optima AOAs. First, grey wolf optimization incorporated AOAs, where group hunting behavior GWO allows multiple individuals perform searches at same time, enabling solution be more finely tuned avoiding over-concentration particular region, which can improve capability AOA. Second, end each run, follower mechanism Cauchy mutation operation Sparrow Search are selected with probability perturbed enhance escape from optimum. overall performance improved algorithm assessed selecting 23 benchmark functions using Wilcoxon rank-sum test. results HIAOA compared other intelligent algorithms. Furthermore, also three design problems successfully, demonstrating competitiveness. According experimental results, better test than comparator.

Language: Английский

Advancing Engineering Solutions with Protozoa-Based Differential Evolution: A Hybrid Optimization Approach DOI Creative Commons
Hussam N. Fakhouri, Faten Hamad, Abdelraouf Ishtaiwi

et al.

Automation, Journal Year: 2025, Volume and Issue: 6(2), P. 13 - 13

Published: March 28, 2025

This paper presents a novel Hybrid Artificial Protozoa Optimizer with Differential Evolution (HPDE), combining the biologically inspired principles of (APO) powerful optimization strategies (DE) to address complex and engineering design challenges. The HPDE algorithm is designed balance exploration exploitation features, utilizing innovative features such as autotrophic heterotrophic foraging behaviors, dormancy, reproduction processes alongside DE strategy. performance was evaluated on CEC2014 benchmark functions, it compared against two sets state-of-the-art optimizers comprising 23 different algorithms. results demonstrate HPDE’s good performance, outperforming competitors in 24 functions out 30 from first set second set. Additionally, has been successfully applied range problems, including robot gripper optimization, welded beam pressure vessel spring speed reducer cantilever three-bar truss optimization. consistently showcase solving these problems when competing

Language: Английский

Citations

0
SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study DOI Creative Commons
Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 30, 2025

In this study, we propose a novel approach for breast cancer classification that integrates the Seagull Optimization Algorithm (SGA) feature selection with Random Forest (RF) classifier effective data classification. The novelty of our lies in first-time application SGA gene diagnosis, where systematically explores space to identify most informative subsets, thereby improving accuracy and reducing computational complexity. selected features are subsequently classified using RF, known its robustness high handling complex datasets. To evaluate effectiveness proposed method, compared it other classifiers, including Linear Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN). SGA-RF combination achieved best mean 99.01% 22 genes, outperforming methods demonstrating consistent performance across varying subsets. accuracies ranged from 85.35 94.33%, highlighting balance between reduction accuracy. Future work will explore integration nature-inspired algorithms deep learning models further enhance clinical applicability.

Language: Английский

Citations

0
Energy Consumption Prediction and Optimization of Electric Vehicles Based on RLS and Improved SOA DOI Creative Commons
Chunling Liu

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 38180 - 38191

Published: Jan. 1, 2024

The new energy vehicle industry is facing challenges. To predict and optimize the consumption of electric vehicles, this study predicts based on characteristics power system air conditioning system, combines path optimization algorithms for energy-saving planning. first improves recursive least squares algorithm by combining forgetting factor, constructs a identification model improved neural network. Then, seagull established using chaotic mapping strategy t-distribution to improve algorithm. results showed that predicted final constructed in was 2.81kW.h, with an error rate 5.1%. obtained optimal solution 30.88m burma14 423.74m oliver30, which were consistent published solutions. When turned on, selected reduced about 5.6%. Under condition not turning conditioning, 4.98%. In summary, through research has good application effects predicting optimizing consumption. contribution lies it helps reveal laws utilization economy, safety, environmental friendliness vehicles during operation, promote overall management vehicles.

Language: Английский

Citations

3
Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems DOI
Kamran Rezaei, Omid Solaymani Fard

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 159, P. 111650 - 111650

Published: April 30, 2024

Language: Английский

Citations

3
Artificial bee colony algorithm based on multiple indicators for many-objective optimization with irregular Pareto fronts DOI
Hui Wang, Dong Xiao, Shahryar Rahnamayan

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125613 - 125613

Published: Oct. 1, 2024

Language: Английский

Citations

3
Optimal feature selection and invasive weed tunicate swarm algorithm-based hierarchical attention network for text classification DOI Creative Commons
Gunjan Singh,

Arpita Nagpal,

Vijendra Singh

et al.

Connection Science, Journal Year: 2023, Volume and Issue: 35(1)

Published: July 10, 2023

Through social media platforms and the internet, world is becoming more connected, producing enormous amounts of data. Also, texts are collected from media, newspapers, user reviews products, company press releases, etc. The correctness classification mainly dependent on kind words utilised in corpus features for classification. Hence, due to increasing growth text data Internet, accurate organisation management has become a great challenge. this research, an effective Invasive Weed Tunicate Swarm Optimization-based Hierarchical Attention Network (IWTSO-based HAN) implemented achieving categorisation text. Here, mined thereby optimal acquired perform strategy. incorporation parametric each optimisation ensures proposed method increase convergence global solutions by improving effectiveness. obtained better performance with measures, like accuracy, True Positive Rate (TRP), Negative (TNR), precision, False (FNR) values 92.4%, 94.1%, 95.4%, 0.0758.

Language: Английский

Citations

8
Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations DOI Creative Commons
Amin Abdollahi Dehkordi, Bahareh Etaati, Mehdi Neshat

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 39269 - 39294

Published: Jan. 1, 2023

Meta-heuristic algorithms have been effectively employed to tackle a wide range of optimisation issues, including structural engineering challenges. The the shape and size large-scale truss structures is difficult due nonlinear interplay between cross-sectional nodal coordinate pressures structures. Recently, it was demonstrated that newly proposed Marine Predator Algorithm (MPA) performs very well on mathematical MPA meta-heuristic simulates essential hunting habits natural marine predators. However, this algorithm has some disadvantages, such as becoming locked in locally optimal solutions not exhibiting high level exploratory behaviour. This paper proposes two hybrid predator algorithms, Nonlinear (HNMPA) Nonlinear-Chaotic (HNCMPA), improved variations paired with hill-climbing (HC) technique for form size. major advantage these techniques are they seek overcome MPA's disadvantages by using values prolonging exploration phase chaotic values; also, HC used avoid optimum solutions. In terms performance, compared fourteen well-known meta-heuristics, Dragonfly (DA), Henry Gas Solubility (HGSO), Arithmetic (AOA), Generalized Normal Distribution Optimisation (GNDO), Salp Swarm (SSA), Predators (MPA), Neural Network (NNA), Water Cycle (WCA), Artificial Gorilla Troops Optimiser (GTO), Gray Wolf (GWO), Moth Flame (MFO), Multi-Verse (MVO), Equilibrium (EO), Cheetah (CO). Furthermore, seven were chosen test HNCMPA performance benchmark sets, MPA, MVO, PSO, MFO, SSA, GWO, WOA. results experiment demonstrate put forth surpass previously established meta-heuristics field optimisation, encompassing both traditional CEC problems, margin over 95% attaining superior ultimate solution. Additionally, regards solving difficulties real-world challenge, outcomes indicate boost 65% obtaining significantly better problems involving 260-bar 314-bar; conversely, case 340-bar improvement rate slightly lower at almost 25%.

Language: Английский

Citations

7
APFA: Ameliorated Pathfinder Algorithm for Engineering Applications DOI

Keyu Zhong,

Fen Xiao, Xieping Gao

et al.

Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: 21(3), P. 1592 - 1616

Published: April 12, 2024

Language: Английский

Citations

2
Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems DOI Creative Commons

Leela Kumari,

Vikram Kumar Kamboj,

S. K. Bath

et al.

Complex & Intelligent Systems, Journal Year: 2022, Volume and Issue: 9(2), P. 1525 - 1582

Published: Sept. 21, 2022

The existing slime mould algorithm clones the uniqueness of phase oscillation conduct and exhibits slow convergence in local search space due to poor exploitation phase. This research work discover best solution for objective function by commingling simulated annealing better variation parameters named as hybridized algorithm-simulated algorithm. improves accelerates effectiveness technique well assists take off from optimum. To corroborate worth usefulness introduced strategy, nonconvex, nonlinear, typical engineering design difficulties were analyzed standard benchmarks interdisciplinary concerns. proposed version is used evaluate six, five, five unimodal, multimodal fixed-dimension benchmark functions, respectively, also including 11 kinds difficulties. technique's outcomes compared results other on-hand optimization methods, experimental show that suggested approach outperforms techniques.

Language: Английский

Citations

9
A Hybrid-Strategy-Improved Dragonfly Algorithm for the Parameter Identification of an SDM DOI Open Access
Jianping Zhao, Damin Zhang,

Qing He

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(15), P. 11791 - 11791

Published: July 31, 2023

As primary components of solar power applications, photovoltaic cells have promising development prospects. Due to the characteristics PV cells, identification parameters for circuit models has become a research focus. Among various methods parameter estimations, metaheuristic algorithms attracted significant interest. In this paper, hybrid-strategy-improved dragonfly algorithm (HIDA) is proposed meet demand high parameter-identification accuracy. Tent chaotic mapping generates initial position individual dragonflies and aids in increasing population diversity. Individual can adapt their updated positions scenarios using adjacent decision approach. The whale optimization fusion strategy incorporates spiral bubble-net attack mechanism into improve optimization-seeking precision. Moreover, optimal perturbation reduces frequency HIDA falling local optima from perspective an solution. effectiveness was evaluated function test experiments engineering application experiments. Seven unimodal five multimodal benchmark functions 50, 120, 200 dimensions were used experiments, while CEC2013 seven CEC2014 also selected applied single-diode model (SDM), model, double-diode (DDM), triple-diode (TDM), STM-40/36 identification, as well solution classical problems. experimental results all verify good performance with stability, wide range,

Language: Английский

Citations

4