Aug 2026· Concurrency and Computation· Vol 38· 0 citations· 51 references
Abstract
This paper proposes a Hierarchical and Stagnation‐Aware Particle Swarm Optimization with Elite‐Guided Crossover (HECO‐PSO). HECO‐PSO dynamically partitions the swarm into multiple parallel layers, enabling independent exploration and periodic information exchange to mitigate premature convergence. Within each layer, elite‐guided crossover integrates a particle's best position with that of the layer's elite particle, generating high‐quality guiding vectors to enhance solution quality and maintain diversity. Building on this hierarchical framework, a stagnation‐aware strategy adaptively intensifies guidance: prolonged stagnation activates crossover with the global elite, while severe stagnation directly assigns the global best guiding vector, thereby strengthening the ability to escape local optima. Experimental results on the CEC 2017 benchmark suite demonstrate HECO‐PSO's competitive convergence speed and solution accuracy. Its effectiveness is further validated through a real‐world UNSW‐NB15 feature selection task, confirming adaptability and competitiveness in practical applications.
Particle swarm optimization (PSO) is a widely used bio-inspired optimization method. However, global guidance can align particle trajectories, reduce population diversity, and lead to premature convergence. To address this issue, we propose GRACE-PSO, a particle swarm optimizer based on group rank assessment and cooper...
Experimental results demonstrate that the proposed Multi-swarm Collaborative Particle Swarm Optimization with Adaptive Learning Strategy with Adaptive Learning Mechanism exhibits significant advantages in convergence speed, solving accuracy, and comprehensive optimization capabilities compared with several state-of-the...
Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-...
Yao-Pei Wang, Yufeng Wang, Ke Liu· Algorithms· 0 citations
Particle Swarm Optimization (PSO) remains highly effective on many continuous optimization problems, yet its search dynamics collapse too quickly on high-dimensional and deceptive landscapes. Our earlier work introduced single-role diversity-enhancing PSO variants and showed that diversity injection is effective only w...
Piotr Urbańczyk, Aleksandra Urbanczyk, Leszek Rutkowski et al.· Vietnam Journal of Computer...· 0 citations
Cuckoo Search Algorithm (CSA) has emerged as a powerful bio-inspired metaheuristic, yet its reliance on random-walk search often leads to slow convergence and susceptibility to local optima. This study introduces an Adaptive Elite Cuckoo Search Algorithm (AECSA), designed to enhance both convergence speed and solution...
P. Ong, Jia-Hang Wu· Journal of Computing Researc...· 0 citations
Traditional portfolio optimization models have limitations in addressing high-dimensional non-convex issues and complex real-world constraints, such as parameter sensitivity, susceptibility to local optima, and strong assumptions. To achieve more robust and efficient asset allocation, this paper constructs a hybrid opt...
