HPB3C-3PG algorithm: A new hybrid global optimization algorithm and its application to plant classification

• Published in: Ecological informatics Vol. 81; p. 102581
• Main Authors: Ghosh, Sukanta, Singh, Amar, Kumar, Shakti
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2024
• Subjects: 3PGA; CEC’21 benchmark function; Exploitation; Exploration; HPB3C-3PGA; Hybrid algorithm; Image classification; Optimization problem; PB3C; Hybrid algorithm; HPB3C-3PGA; CEC’21 benchmark function; PB3C; Exploration; Optimization problem; 3PGA; Exploitation; Image classification
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2024.102581

This paper proposes a hybrid bio-inspired search and optimization algorithm that combines the strengths of the PB3C (Parallel Big Bang Big Crunch) and 3PGA (3 Parent Genetic Algorithm) algorithms. The hybrid algorithm employs a single population-based evolutionary search coupled with multi-population parallel processing techniques to address optimization problems. The proposed algorithm is implemented in MATLAB software. We evaluate the performance of the proposed algorithm on the CEC2021 standard test bench suite. The performance of the proposed approach is compared with that of the other nine algorithms. The comparative analysis shows that the proposed hybrid PB3C and 3PGA algorithms performed better than the other nine optimization algorithms. Furthermore, this chapter proposes an HPB3C-3PGA-based approach to evolve the near-optimal architecture of CNN. The proposed plant image classification approach is implemented in Python and compared with 12 other approaches. The proposed approach achieved an accuracy of 98.96% on the Mendeley dataset and 98.97% on the CVIP100 dataset. The proposed approach outperforms all other approaches for the plant leaf classification problem. This research significantly contributes to overcoming limitations in existing approaches, providing a robust solution for optimization problems and image classification tasks. •Proposed hybrid algorithm combines PB3C and 3PGA strengths for optimization problems.•Tested on CEC2021 benchmark functions, outperformed nine existing algorithms.•Synergy of parallel processing and advanced genetic operators drives success.•Promising solution for real-world applications requiring efficient optimization techniques.•Achieved impressive accuracy of 98.96% in plant leaf image classification.