Hao Chen¹, Tong Xu¹, Yutian Huang², Dabo Xin^1,*, Changting Zhong^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075792 - 29 January 2026

Abstract Optimization problems are prevalent in various fields of science and engineering, with several real-world applications characterized by high dimensionality and complex search landscapes. Starfish optimization algorithm (SFOA) is a recently optimizer inspired by swarm intelligence, which is effective for numerical optimization, but it may encounter premature and local convergence for complex optimization problems. To address these challenges, this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm (MCPSFOA). The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA, which integrates the exploratory mechanisms of SFOA with the diverse search capacity of… More >

Wanwei Huang^1,*, Huicong Yu¹, Jiawei Ren², Kun Wang³, Yanbu Guo¹, Lifeng Jin⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068493 - 10 November 2025

Abstract Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity. These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy. This paper proposes an industrial Internet of Things intrusion detection feature selection algorithm based on an improved whale optimization algorithm (GSLDWOA). The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to, such as local optimality, long detection time, and reduced accuracy. First, the initial population’s diversity is increased using the Gaussian Mutation More >

Dongping Tian^a,b

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 331-342, 2018, DOI:10.1080/10798587.2017.1293881

Abstract Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been deeply studied and widely applied to a variety of problems. However, it is easily trapped into the local optima and premature convergence appears when solving complex multimodal problems. To address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed particles to improve the quality of the… More >