A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization

Abstract

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.