A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization
The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. H...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/17/2/53 |
| _version_ | 1797299165822189568 |
|---|---|
| author | Abdullahi T. Sulaiman Habeeb Bello-Salau Adeiza J. Onumanyi Muhammed B. Mu’azu Emmanuel A. Adedokun Ahmed T. Salawudeen Abdulfatai D. Adekale |
| author_facet | Abdullahi T. Sulaiman Habeeb Bello-Salau Adeiza J. Onumanyi Muhammed B. Mu’azu Emmanuel A. Adedokun Ahmed T. Salawudeen Abdulfatai D. Adekale |
| author_sort | Abdullahi T. Sulaiman |
| collection | DOAJ |
| description | The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO’s particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling. |
| first_indexed | 2024-03-07T22:45:44Z |
| format | Article |
| id | doaj.art-924a742b6b464b2fad2fed41833373a5 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-07T22:45:44Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-924a742b6b464b2fad2fed41833373a52024-02-23T15:04:26ZengMDPI AGAlgorithms1999-48932024-01-011725310.3390/a17020053A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced OptimizationAbdullahi T. Sulaiman0Habeeb Bello-Salau1Adeiza J. Onumanyi2Muhammed B. Mu’azu3Emmanuel A. Adedokun4Ahmed T. Salawudeen5Abdulfatai D. Adekale6Department of Computer Engineering, Ahmadu Bello University Zaria, Zaria 810107, NigeriaDepartment of Computer Engineering, Ahmadu Bello University Zaria, Zaria 810107, NigeriaNext Generation Enterprises and Institutions, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South AfricaDepartment of Computer Engineering, Ahmadu Bello University Zaria, Zaria 810107, NigeriaDepartment of Computer Engineering, Ahmadu Bello University Zaria, Zaria 810107, NigeriaDepartment of Electrical and Electronics Engineering, University of Jos, Jos 930003, NigeriaDepartment of Computer Engineering, Ahmadu Bello University Zaria, Zaria 810107, NigeriaThe particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO’s particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling.https://www.mdpi.com/1999-4893/17/2/53benchmarkoptimalparticle swarm optimizationsmell agent optimizationsolutiontest functions |
| spellingShingle | Abdullahi T. Sulaiman Habeeb Bello-Salau Adeiza J. Onumanyi Muhammed B. Mu’azu Emmanuel A. Adedokun Ahmed T. Salawudeen Abdulfatai D. Adekale A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization Algorithms benchmark optimal particle swarm optimization smell agent optimization solution test functions |
| title | A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization |
| title_full | A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization |
| title_fullStr | A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization |
| title_full_unstemmed | A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization |
| title_short | A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization |
| title_sort | particle swarm and smell agent based hybrid algorithm for enhanced optimization |
| topic | benchmark optimal particle swarm optimization smell agent optimization solution test functions |
| url | https://www.mdpi.com/1999-4893/17/2/53 |
| work_keys_str_mv | AT abdullahitsulaiman aparticleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT habeebbellosalau aparticleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT adeizajonumanyi aparticleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT muhammedbmuazu aparticleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT emmanuelaadedokun aparticleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT ahmedtsalawudeen aparticleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT abdulfataidadekale aparticleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT abdullahitsulaiman particleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT habeebbellosalau particleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT adeizajonumanyi particleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT muhammedbmuazu particleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT emmanuelaadedokun particleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT ahmedtsalawudeen particleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization AT abdulfataidadekale particleswarmandsmellagentbasedhybridalgorithmforenhancedoptimization |