Enhanced honey badger algorithm based on nonlinear adaptive weight and golden sine operator

The honey badger algorithm (HBA) is a swarm intelligence algorithm that imitates honey badgers’ intelligent foraging techniques. HBA diversifies and intensifies the search space by simulating digging and honey-finding strategies. However, HBA suffers from slow convergence speed, imbalanced diversifi...

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Bibliographic Details
Published in:Neural computing & applications Vol. 37; no. 1; pp. 367 - 386
Main Authors: Majumdar, Parijata, Mitra, Sanjoy
Format: Journal Article
Language:English
Published: London Springer London 01.01.2025
Springer Nature B.V
Subjects:
Adaptive algorithms
Algorithms
Artificial Intelligence
Communication
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Convergence
Data Mining and Knowledge Discovery
Design engineering
Diversification
Evolution
Heuristic
Image Processing and Computer Vision
Mathematical programming
Neighborhoods
Neural networks
Optimization algorithms
Original Article
Performance evaluation
Probability and Statistics in Computer Science
Searching
Statistical analysis
Swarm intelligence
Unmanned aerial vehicles
Global convergence
Non-linear adaptive weight
Honey Badger algorithm
Golden sine operator
Optimization
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:The honey badger algorithm (HBA) is a swarm intelligence algorithm that imitates honey badgers’ intelligent foraging techniques. HBA diversifies and intensifies the search space by simulating digging and honey-finding strategies. However, HBA suffers from slow convergence speed, imbalanced diversification, and intensification problems. Therefore, we developed the nonlinear adaptive weight and the golden sine operator-based enhanced HBA (NGS-eHBA). The newly added nonlinear adaptive weight explores the search space adaptively, balancing its diversification and intensification. Next, we incorporate the improved golden sine operator to establish a sine route that accelerates the global convergence speed during the search. We compare NGS-eHBA with recent optimization algorithms using well-known benchmark functions for performance evaluation, and statistical analyses show that it outperforms other algorithms. We also use the NGS-eHBA algorithm to resolve engineering design problems, where it outperforms other algorithms noticeably.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-10484-9