Particle Swarm Optimization Algorithm for Feature Selection Inspired by Peak Ecosystem Dynamics
Shaobo Deng*, Meiru Xie, Bo Wang, Shuaikun Zhang, Sujie Guan, Min Li
School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
* Corresponding Author: Shaobo Deng. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057874
Received 29 August 2024; Accepted 20 November 2024; Published online 13 December 2024
Abstract
In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.
Keywords
Machine learning; feature selection; evolutionary algorithm; particle swarm optimization
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