Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108803 - 108803

Published: July 1, 2024

Language: Английский

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A Halton Enhanced Solution-based Human Evolutionary Algorithm for Complex Optimization and Advanced Feature Selection Problems

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113062 - 113062

Published: Jan. 1, 2025

Language: Английский

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Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124882 - 124882

Published: July 29, 2024

Language: Английский

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A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(33), P. 20723 - 20750

Published: Aug. 16, 2024

Abstract Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors growth, leading suboptimal predictions. Consequently, identifying most important feature vital when leveraging Support Vector Regressor (SVR) prediction. In addition, manual tuning of SVR hyperparameters may not always offer high accuracy. this paper, we introduce a novel framework yields that address these challenges. Our integrates new hybrid selection approach with an optimized model enhance prediction accuracy efficiently. The proposed comprises three phases: preprocessing, selection, phases. preprocessing phase, data normalization conducted, followed by application K-means clustering in conjunction correlation-based filter (CFS) generate reduced dataset. Subsequently, FMIG-RFE proposed. Finally, phase introduces improved variant Crayfish Optimization Algorithm (COA), named ICOA, which utilized optimize thereby achieving superior along approach. Several experiments are conducted assess evaluate performance framework. results demonstrated over state-of-art approaches. Furthermore, experimental findings regarding ICOA optimization algorithm affirm its efficacy model, enhancing both computational efficiency, surpassing algorithms.

Language: Английский

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An improved multi-strategy Golden Jackal algorithm for real world engineering problems

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 295, P. 111725 - 111725

Published: April 16, 2024

Language: Английский

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A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Biomimetics, Journal Year: 2024, Volume and Issue: 9(5), P. 270 - 270

Published: April 28, 2024

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened ability in the process of finding optimal solution. At same time, it is easy to fall into local extremes other limitations. In this paper, a novel (SCMGJO) combining sine–cosine Cauchy mutation proposed. On one hand, tent mapping reverse learning introduced population initialization, sine cosine strategies are update prey positions, which enhances global exploration algorithm. introduction for perturbation solution effectively improves algorithm’s obtain Through experiment 23 benchmark test functions, results show that SCMGJO performs well speed accuracy. addition, stretching/compression spring design problem, three-bar truss unmanned aerial vehicle path planning problem verification. experimental prove superior performance compared with intelligent algorithms verify its application engineering applications.

Language: Английский

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An improved Genghis Khan optimizer based on enhanced solution quality strategy for global optimization and feature selection problems

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 302, P. 112347 - 112347

Published: Aug. 5, 2024

Language: Английский

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Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection

Biomimetics, Journal Year: 2025, Volume and Issue: 10(1), P. 53 - 53

Published: Jan. 14, 2025

Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights with cryptobiosis differential evolution (CPLODE), novel improvement upon original (PLO) algorithm. CPLODE integrates mechanism (DE) operators to enhance PLO's search capabilities. The particle collision strategy is replaced DE's mutation crossover operators, enabling more effective exploration using dynamic rate improve convergence. Furthermore, records reuses historically successful solutions, thereby improving greedy process. experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE's superior performance compared eight classical algorithms, higher average ranks faster Moreover, achieved competitive ten real-world datasets, outperforming several well-known binary metaheuristic classification accuracy reduction. These highlight effectiveness for both selection.

Language: Английский

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Intelligent and Secure Evolved Framework for Vaccine Supply Chain Management Using Machine Learning and Blockchain

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(2)

Published: Jan. 29, 2025

Language: Английский

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Single-stage filter-based local feature selection using an immune algorithm for high-dimensional microarray data

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112895 - 112895

Published: Feb. 1, 2025

Language: Английский

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