Novel Protein–Protein Interaction Prediction with Updated Deep Radial Graph Basis Prism Refraction Search Convolutional Networks Model DOI

S. Nivedha,

Bhavani Sridharan

Journal of Biomedical Nanotechnology, Journal Year: 2024, Volume and Issue: 20(12), P. 1804 - 1823

Published: Dec. 1, 2024

Predicting Protein–Protein Interactions (PPIs) is essential to comprehending biological functions and pivotal for drug discovery disease understanding.However, accurately predicting these interactions remains a difficult issue because of the intricate multifaceted nature protein networks. Traditional models often fail fully capture relationships between proteins their interactions, especially when diverse datasets are involved. To address challenges, novel approach, named Deep Radial Graph Basis Prism Refraction Search Convolutional Networks(DRGB-PRSCN) model, proposed PPI prediction using three distinct datasets: Human PPI, STRING, DIP.The method employs Gradient Domain Guided Filtering effective data preprocessing, ensuring noise reduction while preserving features. Feature extraction carried out an Elastic Decision Transformer, which effectively captures key Networks (DGCNs) leveraged model complex dependencies among proteins. The DRGB-PRSCN with its advanced architecture, employed predict high precision. achieves performance evaluation score 99.9%, demonstrating efficacy in PPI. This approach outperforms traditional methods by providing superior accuracy robustness, making it highly beneficial network analysis discovery. model’s primary benefit capacity efficiently handle PPIs exceptional

Language: Английский

Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Osama Al-Baik, Saleh Ali Alomari,

Omar Alssayed

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(2), P. 65 - 65

Published: Jan. 23, 2024

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates natural behavior of pufferfish in nature, is introduced this paper. The fundamental inspiration POA adapted from defense mechanism against predators. In mechanism, by filling its elastic stomach with water, becomes a spherical ball pointed spines, and as result, hungry predator escapes threat. theory stated then mathematically modeled two phases: (i) exploration based on simulation predator’s attack (ii) exploitation escape spiny pufferfish. performance evaluated handling CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. optimization results show has achieved an effective solution appropriate ability exploration, exploitation, balance between them during search process. quality process compared twelve well-known algorithms. provides superior achieving better most benchmark functions order solve competitor Also, effectiveness handle tasks real-world applications twenty-two constrained problems 2011 four engineering design problems. Simulation solutions

Language: Английский

Citations

29
An Improved Bio-Inspired Material Generation Algorithm for Engineering Optimization Problems Including PV Source Penetration in Distribution Systems DOI Creative Commons
Mona Gamal, Shahenda Sarhan, Ahmed R. Ginidi

et al.

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

Citations

5
Polar fox optimization algorithm: a novel meta-heuristic algorithm DOI
Ahmad Ghiaskar, Amir Mohammadian Amiri, Seyedali Mirjalili

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(33), P. 20983 - 21022

Published: Aug. 21, 2024

Language: Английский

Citations

12
Frilled Lizard Optimization: A Novel Nature-Inspired Metaheuristic Algorithm for Solving Optimization Problems DOI Open Access

Ibraheem Abu Falahah,

Osama Al-Baik, Saleh Ali Alomari

et al.

Published: March 15, 2024

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from sit-and-wait strategy observed during hunting. The underlying theory is presented and mathematically formulated two phases: (i) an exploration phase, simulating lizard's attack towards prey, (ii) exploitation retreat to top tree after feeding. To assess FLO's efficacy solving optimization problems, algorithm's performance evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional CEC 2017 test suite. Comparative analyses with twelve existing algorithms are conducted. simulation results reveal that FLO, distinguished by its adeptness exploration, exploitation, balancing them search process, outperforms competing algorithms. Additionally, implemented on twenty-two constrained problems 2011 suite four engineering design demonstrating effectiveness addressing real-world applications.

Language: Английский

Citations

9
Fungal growth optimizer: A novel nature-inspired metaheuristic algorithm for stochastic optimization DOI

Mohamed Abdel‐Basset,

Reda Mohamed, Mohamed Abouhawwash

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117825 - 117825

Published: Feb. 9, 2025

Language: Английский

Citations

1
Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems DOI Creative Commons
Zoubida Benmamoun,

Khaoula Khlie,

Gulnara Bektemyssova

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 29, 2024

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional strategies often struggle resources the complex dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring random search nature emphasizing that no algorithm best optimizer all applications, No Free Lunch (NFL) theorem encourages researchers design newer be able provide more effective solutions Motivated by NFL theorem, innovation novelty this paper designing new meta-heuristic called Bobcat Optimization Algorithm (BOA) imitates natural behavior bobcats wild. The basic inspiration BOA derived from hunting strategy during attack towards prey chase process between them. theory stated then mathematically modeled two phases (i) exploration based on simulation bobcat's position change while moving (ii) exploitation simulating catch prey. performance evaluated handle CEC 2017 test suite problem dimensions equal 10, 30, 50, 100, well address 2020. results show has high ability exploration, exploitation, balance them order achieve suitable solution obtained compared with twelve well-known algorithms. findings been successful handling 89.65, 79.31, 93.10, 89.65% functions dimension respectively. Also, 2020 suite, 100% suite. statistical analysis confirms significant superiority competition analyze dealing real world twenty-two constrained problems 2011 four engineering selected. 90.90% CEC2011 addition, SCM applications challenged ten case studies field sustainable lot size optimization. successfully provided superior competitor

Language: Английский

Citations

7
Interpersonal Sensitivity Prediction Based on Multi-strategy Artemisinin Optimization with Fuzzy K-Nearest Neighbor DOI
Yingjie Tian, Xiao Pan,

Xinsen Zhou

et al.

Journal of Bionic Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 2025

Language: Английский

Citations

0
Metaheuristic-Based Neuroevolution Framework for Improved Pneumonia Classification in X-ray Images DOI
Diego Peña, Oscar Ramos-Soto,

Javier Augusto Galvis-Chacon

et al.

Studies in computational intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 393 - 427

Published: Jan. 1, 2025

Language: Английский

Citations

0
Random Walk‐Based GOOSE Algorithm for Solving Engineering Structural Design Problems DOI Creative Commons

S. Mounika,

Himanshu Sharma, A. Krishna

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(5)

Published: April 30, 2025

ABSTRACT The proposed Random Walk‐based Improved GOOSE (IGOOSE) search algorithm is a novel population‐based meta‐heuristic inspired by the collective movement patterns of geese and stochastic nature random walks. This includes inherent balance between exploration exploitation integrating walk behavior with local strategies. In this paper, IGOOSE has been rigorously tested across 23 benchmark functions where 13 benchmarks are varying dimensions (10, 30, 50, 100 dimensions). These provide diverse range optimization landscapes, enabling comprehensive evaluation performance under different problem complexities. various parameters such as convergence speed, magnitude solution, robustness for dimensions. Further, applied to optimize eight distinct engineering problems, showcasing its versatility effectiveness in real‐world scenarios. results these evaluations highlight competitive tool, offering promising both standard complex structural problems. Its ability effectively, combined deal positions valuable tool.

Language: Английский

Citations

0
Escape after love: Philoponella prominens optimizer and its application to 3D path planning DOI
Yuansheng Gao, Jinpeng Wang, Changlin Li

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 28(2)

Published: Nov. 26, 2024

Language: Английский

Citations

3